{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D7tqLMoKF6uq"
   },
   "source": [
    "Deep Learning\n",
    "=============\n",
    "\n",
    "Assignment 5\n",
    "------------\n",
    "\n",
    "The goal of this assignment is to train a Word2Vec skip-gram model over [Text8](http://mattmahoney.net/dc/textdata) data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "0K1ZyLn04QZf"
   },
   "outputs": [],
   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "%matplotlib inline\n",
    "from __future__ import print_function\n",
    "import collections\n",
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import tensorflow as tf\n",
    "import zipfile\n",
    "from matplotlib import pylab\n",
    "from six.moves import range\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aCjPJE944bkV"
   },
   "source": [
    "Download the data from the source website if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 14640,
     "status": "ok",
     "timestamp": 1445964482948,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2f1ffade4c9f20de",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "RJ-o3UBUFtCw",
    "outputId": "c4ec222c-80b5-4298-e635-93ca9f79c3b7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found and verified text8.zip\n"
     ]
    }
   ],
   "source": [
    "url = 'http://mattmahoney.net/dc/'\n",
    "\n",
    "def maybe_download(filename, expected_bytes):\n",
    "  \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
    "  if not os.path.exists(filename):\n",
    "    filename, _ = urlretrieve(url + filename, filename)\n",
    "  statinfo = os.stat(filename)\n",
    "  if statinfo.st_size == expected_bytes:\n",
    "    print('Found and verified %s' % filename)\n",
    "  else:\n",
    "    print(statinfo.st_size)\n",
    "    raise Exception(\n",
    "      'Failed to verify ' + filename + '. Can you get to it with a browser?')\n",
    "  return filename\n",
    "\n",
    "filename = maybe_download('text8.zip', 31344016)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zqz3XiqI4mZT"
   },
   "source": [
    "Read the data into a string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 28844,
     "status": "ok",
     "timestamp": 1445964497165,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2f1ffade4c9f20de",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "Mvf09fjugFU_",
    "outputId": "e3a928b4-1645-4fe8-be17-fcf47de5716d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['text8']\n",
      "Data size 17005207\n"
     ]
    }
   ],
   "source": [
    "def read_data(filename):\n",
    "  \"\"\"Extract the first file enclosed in a zip file as a list of words\"\"\"\n",
    "  with zipfile.ZipFile(filename) as f:\n",
    "    print(f.namelist())\n",
    "    data = tf.compat.as_str(f.read(f.namelist()[0])).split()\n",
    "  return data\n",
    "  \n",
    "words = read_data(filename)\n",
    "print('Data size %d' % len(words))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zdw6i4F8glpp"
   },
   "source": [
    "Build the dictionary and replace rare words with UNK token."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
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      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 28849,
     "status": "ok",
     "timestamp": 1445964497178,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2f1ffade4c9f20de",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "gAL1EECXeZsD",
    "outputId": "3fb4ecd1-df67-44b6-a2dc-2291730970b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common words (+UNK) [['UNK', 418391], ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764)]\n",
      "Sample data [5239, 3084, 12, 6, 195, 2, 3137, 46, 59, 156]\n"
     ]
    }
   ],
   "source": [
    "vocabulary_size = 50000\n",
    "\n",
    "def build_dataset(words):\n",
    "  count = [['UNK', -1]]\n",
    "  count.extend(collections.Counter(words).most_common(vocabulary_size - 1))\n",
    "  dictionary = dict()\n",
    "  for word, _ in count:\n",
    "    dictionary[word] = len(dictionary)\n",
    "  data = list()\n",
    "  unk_count = 0\n",
    "  for word in words:\n",
    "    if word in dictionary:\n",
    "      index = dictionary[word]\n",
    "    else:\n",
    "      index = 0  # dictionary['UNK']\n",
    "      unk_count = unk_count + 1\n",
    "    data.append(index)\n",
    "  count[0][1] = unk_count\n",
    "  reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys())) \n",
    "  return data, count, dictionary, reverse_dictionary\n",
    "\n",
    "data, count, dictionary, reverse_dictionary = build_dataset(words)\n",
    "print('Most common words (+UNK)', count[:5])\n",
    "print('Sample data', data[:10])\n",
    "del words  # Hint to reduce memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lFwoyygOmWsL"
   },
   "source": [
    "Function to generate a training batch for the skip-gram model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 113,
     "status": "ok",
     "timestamp": 1445964901989,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2f1ffade4c9f20de",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "w9APjA-zmfjV",
    "outputId": "67cccb02-cdaf-4e47-d489-43bcc8d57bb8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first']\n",
      "\n",
      "with num_skips = 2 and skip_window = 1:\n",
      "    batch: ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term']\n",
      "    labels: ['as', 'anarchism', 'originated', 'a', 'as', 'term', 'a', 'of']\n",
      "\n",
      "with num_skips = 4 and skip_window = 2:\n",
      "    batch: ['as', 'as', 'as', 'as', 'a', 'a', 'a', 'a']\n",
      "    labels: ['a', 'anarchism', 'originated', 'term', 'term', 'as', 'originated', 'of']\n"
     ]
    }
   ],
   "source": [
    "data_index = 0\n",
    "\n",
    "def generate_batch(batch_size, num_skips, skip_window):\n",
    "  global data_index\n",
    "  assert batch_size % num_skips == 0\n",
    "  assert num_skips <= 2 * skip_window\n",
    "  batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "  labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "  span = 2 * skip_window + 1 # [ skip_window target skip_window ]\n",
    "  buffer = collections.deque(maxlen=span)\n",
    "  for _ in range(span):\n",
    "    buffer.append(data[data_index])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "  for i in range(batch_size // num_skips):\n",
    "    target = skip_window  # target label at the center of the buffer\n",
    "    targets_to_avoid = [ skip_window ]\n",
    "    for j in range(num_skips):\n",
    "      while target in targets_to_avoid:\n",
    "        target = random.randint(0, span - 1)\n",
    "      targets_to_avoid.append(target)\n",
    "      batch[i * num_skips + j] = buffer[skip_window]\n",
    "      labels[i * num_skips + j, 0] = buffer[target]\n",
    "    buffer.append(data[data_index])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "  return batch, labels\n",
    "\n",
    "print('data:', [reverse_dictionary[di] for di in data[:8]])\n",
    "\n",
    "for num_skips, skip_window in [(2, 1), (4, 2)]:\n",
    "    data_index = 0\n",
    "    batch, labels = generate_batch(batch_size=8, num_skips=num_skips, skip_window=skip_window)\n",
    "    print('\\nwith num_skips = %d and skip_window = %d:' % (num_skips, skip_window))\n",
    "    print('    batch:', [reverse_dictionary[bi] for bi in batch])\n",
    "    print('    labels:', [reverse_dictionary[li] for li in labels.reshape(8)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ofd1MbBuwiva"
   },
   "source": [
    "Train a skip-gram model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "8pQKsV4Vwlzy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/nn_impl.py:1344: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See @{tf.nn.softmax_cross_entropy_with_logits_v2}.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "embedding_size = 128 # Dimension of the embedding vector.\n",
    "skip_window = 1 # How many words to consider left and right.\n",
    "num_skips = 2 # How many times to reuse an input to generate a label.\n",
    "# We pick a random validation set to sample nearest neighbors. here we limit the\n",
    "# validation samples to the words that have a low numeric ID, which by\n",
    "# construction are also the most frequent. \n",
    "valid_size = 16 # Random set of words to evaluate similarity on.\n",
    "valid_window = 100 # Only pick dev samples in the head of the distribution.\n",
    "valid_examples = np.array(random.sample(range(valid_window), valid_size))\n",
    "num_sampled = 64 # Number of negative examples to sample.\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default(), tf.device('/cpu:0'):\n",
    "\n",
    "  # Input data.\n",
    "  train_dataset = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "  valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n",
    "  \n",
    "  # Variables.\n",
    "  embeddings = tf.Variable(\n",
    "    tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
    "  softmax_weights = tf.Variable(\n",
    "    tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                         stddev=1.0 / math.sqrt(embedding_size)))\n",
    "  softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "  \n",
    "  # Model.\n",
    "  # Look up embeddings for inputs.\n",
    "  embed = tf.nn.embedding_lookup(embeddings, train_dataset)\n",
    "  # Compute the softmax loss, using a sample of the negative labels each time.\n",
    "  loss = tf.reduce_mean(\n",
    "    tf.nn.sampled_softmax_loss(weights=softmax_weights, biases=softmax_biases, inputs=embed,\n",
    "                               labels=train_labels, num_sampled=num_sampled, num_classes=vocabulary_size))\n",
    "\n",
    "  # Optimizer.\n",
    "  # Note: The optimizer will optimize the softmax_weights AND the embeddings.\n",
    "  # This is because the embeddings are defined as a variable quantity and the\n",
    "  # optimizer's `minimize` method will by default modify all variable quantities \n",
    "  # that contribute to the tensor it is passed.\n",
    "  # See docs on `tf.train.Optimizer.minimize()` for more details.\n",
    "  optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)\n",
    "  \n",
    "  # Compute the similarity between minibatch examples and all embeddings.\n",
    "  # We use the cosine distance:\n",
    "  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True))\n",
    "  normalized_embeddings = embeddings / norm\n",
    "  valid_embeddings = tf.nn.embedding_lookup(\n",
    "    normalized_embeddings, valid_dataset)\n",
    "  similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cellView": "both",
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      "startup": false,
      "wait_interval": 0
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      },
      {
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     ]
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    "colab_type": "code",
    "executionInfo": {
     "elapsed": 436189,
     "status": "ok",
     "timestamp": 1445965429787,
     "user": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Average loss at step 0: 7.598526\n",
      "Nearest to during: yakuza, okinawa, audiovisual, rolls, pfaff, gagarin, medal, koi,\n",
      "Nearest to history: cuc, uranhay, joins, moldovans, reboot, renovate, plucked, sacco,\n",
      "Nearest to by: pregnant, largo, uz, krona, haihowak, freescale, meriwether, distributes,\n",
      "Nearest to its: proclaiming, personalized, ducal, inge, feature, conner, espouse, competed,\n",
      "Nearest to first: surreal, chic, unequivocally, peggy, pedagogical, utilising, firsthand, denigrating,\n",
      "Nearest to with: skiego, leno, josip, warbler, overarching, marque, osz, rsted,\n",
      "Nearest to their: ferromagnetic, perp, shogun, corfu, glimpses, douglas, argus, clerks,\n",
      "Nearest to which: belong, bhutan, szl, aurelian, wawel, tutsi, reappeared, byrd,\n",
      "Nearest to not: hcl, laurentian, partly, massey, ruins, cruelty, incrimination, imaginations,\n",
      "Nearest to of: diagnosed, cccc, faint, supreme, abdul, tee, geodesic, dissatisfaction,\n",
      "Nearest to there: moron, bingham, steppe, bauds, tallinn, misogyny, irwin, clump,\n",
      "Nearest to seven: bosom, exposing, hexer, cas, cosmonaut, carries, geological, eurotunnel,\n",
      "Nearest to were: frg, curiae, gibbon, conses, petal, openstep, celebrity, remarry,\n",
      "Nearest to world: polygonal, amendment, pikemen, aluminium, fibrosis, weeks, heat, laertius,\n",
      "Nearest to or: amyl, bypassing, variably, gorgeous, dissipated, awakening, disavowed, myrrh,\n",
      "Nearest to other: cabal, knob, gbit, neither, jump, reissues, damietta, mutations,\n",
      "Average loss at step 2000: 4.376442\n",
      "Average loss at step 4000: 3.862934\n",
      "Average loss at step 6000: 3.793127\n",
      "Average loss at step 8000: 3.687293\n",
      "Average loss at step 10000: 3.615345\n",
      "Nearest to during: pfaff, reproducible, of, hollis, responsive, arcology, adept, rolls,\n",
      "Nearest to history: moldovans, cuc, ideology, joins, juliana, rape, provincial, fidesz,\n",
      "Nearest to by: was, as, into, been, with, on, shaggy, emc,\n",
      "Nearest to its: his, the, their, machete, growers, feature, comnenus, engines,\n",
      "Nearest to first: unequivocally, burial, cockpits, chic, topics, firsthand, sealand, tsr,\n",
      "Nearest to with: in, by, for, walsingham, rammstein, after, streetcar, rotations,\n",
      "Nearest to their: his, the, its, her, torts, subcommittee, shook, teatro,\n",
      "Nearest to which: that, it, maoist, apostolic, this, faso, also, reappeared,\n",
      "Nearest to not: incrimination, who, often, it, also, diputados, vulgaris, squire,\n",
      "Nearest to of: in, groundbreaking, for, jackass, aq, vashem, during, iis,\n",
      "Nearest to there: it, they, often, he, she, principate, jeffries, also,\n",
      "Nearest to seven: six, eight, nine, four, three, five, zero, two,\n",
      "Nearest to were: are, was, have, is, be, stabilised, triads, alligators,\n",
      "Nearest to world: editorial, pikemen, boasted, meo, kanembu, tying, resort, tinymud,\n",
      "Nearest to or: and, sends, george, than, symmetrically, clogged, christina, egalitarianism,\n",
      "Nearest to other: societal, these, different, looney, kislev, sharply, knob, oldenburg,\n",
      "Average loss at step 12000: 3.604962\n",
      "Average loss at step 14000: 3.572487\n",
      "Average loss at step 16000: 3.409205\n",
      "Average loss at step 18000: 3.456966\n",
      "Average loss at step 20000: 3.546195\n",
      "Nearest to during: in, at, pfaff, reproducible, hollis, arminian, arcology, after,\n",
      "Nearest to history: moldovans, cuc, rights, ideology, federation, pds, presidential, joins,\n",
      "Nearest to by: be, into, with, were, was, clipped, for, nongovernmental,\n",
      "Nearest to its: their, his, the, machete, many, some, petrol, her,\n",
      "Nearest to first: unequivocally, second, burial, cockpits, firsthand, aquila, last, buch,\n",
      "Nearest to with: in, at, by, for, after, between, rammstein, stony,\n",
      "Nearest to their: his, its, her, the, some, setback, totem, mormonism,\n",
      "Nearest to which: that, this, these, it, there, also, maoist, who,\n",
      "Nearest to not: often, also, it, still, they, incrimination, you, generally,\n",
      "Nearest to of: for, main, aq, bhangra, iis, jackass, saab, schismatic,\n",
      "Nearest to there: it, they, he, often, which, she, jeffries, still,\n",
      "Nearest to seven: eight, nine, six, four, three, five, zero, two,\n",
      "Nearest to were: are, was, have, had, is, be, by, alligators,\n",
      "Nearest to world: editorial, meo, tinymud, tukey, pikemen, domingue, azerbaijan, ticketing,\n",
      "Nearest to or: and, than, carloman, for, oas, retain, hesitant, modell,\n",
      "Nearest to other: many, different, these, among, are, various, societal, oldenburg,\n",
      "Average loss at step 22000: 3.505248\n",
      "Average loss at step 24000: 3.490875\n",
      "Average loss at step 26000: 3.480783\n",
      "Average loss at step 28000: 3.480594\n",
      "Average loss at step 30000: 3.507817\n",
      "Nearest to during: in, after, at, under, reproducible, although, on, umami,\n",
      "Nearest to history: moldovans, ideology, iterative, rights, list, presidential, cuc, pds,\n",
      "Nearest to by: was, dalit, anthrax, in, clipped, through, under, with,\n",
      "Nearest to its: their, his, the, her, machete, setback, some, engines,\n",
      "Nearest to first: second, last, unequivocally, permafrost, firsthand, burial, cockpits, same,\n",
      "Nearest to with: between, after, rammstein, when, in, under, for, plunges,\n",
      "Nearest to their: his, its, her, the, some, our, many, these,\n",
      "Nearest to which: that, this, who, also, these, disengagement, what, it,\n",
      "Nearest to not: often, they, who, still, completed, generally, overcoming, you,\n",
      "Nearest to of: in, from, charmed, including, and, for, against, vashem,\n",
      "Nearest to there: they, it, he, often, still, she, who, aren,\n",
      "Nearest to seven: eight, nine, five, four, six, three, zero, one,\n",
      "Nearest to were: are, was, had, have, been, jaffna, be, while,\n",
      "Nearest to world: meo, shack, editorial, tinymud, pikemen, satellites, propounded, azerbaijan,\n",
      "Nearest to or: and, ide, carloman, as, including, than, micrometer, sie,\n",
      "Nearest to other: different, various, these, such, some, stages, contemporary, are,\n",
      "Average loss at step 32000: 3.503933\n",
      "Average loss at step 34000: 3.495996\n",
      "Average loss at step 36000: 3.451577\n",
      "Average loss at step 38000: 3.299549\n",
      "Average loss at step 40000: 3.434295\n",
      "Nearest to during: at, after, in, against, before, under, exarch, across,\n",
      "Nearest to history: moldovans, list, iterative, ideology, presidential, development, krieg, cuc,\n",
      "Nearest to by: asahi, be, carboxyl, eurovision, immortals, with, through, affiliate,\n",
      "Nearest to its: their, his, the, her, marcion, machete, setback, our,\n",
      "Nearest to first: second, last, permafrost, next, firsthand, unequivocally, commanding, horst,\n",
      "Nearest to with: between, in, after, when, for, stony, emc, praying,\n",
      "Nearest to their: its, his, her, the, our, my, your, these,\n",
      "Nearest to which: that, this, who, also, it, usually, often, what,\n",
      "Nearest to not: still, they, often, it, completed, simply, also, never,\n",
      "Nearest to of: in, jackass, from, although, aq, exact, for, jargon,\n",
      "Nearest to there: they, it, he, often, she, still, aren, which,\n",
      "Nearest to seven: eight, five, six, nine, four, three, zero, two,\n",
      "Nearest to were: are, have, was, jaffna, be, while, these, those,\n",
      "Nearest to world: shack, meo, bulldogs, scalability, satellites, unclassified, tinymud, parnell,\n",
      "Nearest to or: and, than, playback, but, hesitant, any, meany, elvira,\n",
      "Nearest to other: different, various, unaided, preservation, oldenburg, dagger, gonzalo, drawbacks,\n",
      "Average loss at step 42000: 3.433093\n",
      "Average loss at step 44000: 3.455780\n",
      "Average loss at step 46000: 3.450005\n",
      "Average loss at step 48000: 3.358670\n",
      "Average loss at step 50000: 3.378518\n",
      "Nearest to during: in, after, at, when, under, before, although, against,\n",
      "Nearest to history: list, iterative, designation, enhancers, presidential, retrospect, development, factor,\n",
      "Nearest to by: through, with, under, was, clipped, nongovernmental, histidine, inquisition,\n",
      "Nearest to its: their, his, the, her, setback, s, marcion, whose,\n",
      "Nearest to first: last, second, next, unequivocally, final, viewers, fundamental, earliest,\n",
      "Nearest to with: between, by, plunges, protestant, catapults, when, caucasians, after,\n",
      "Nearest to their: its, his, her, the, your, our, some, both,\n",
      "Nearest to which: that, this, who, also, usually, what, often, actually,\n",
      "Nearest to not: still, never, almost, simply, who, only, always, also,\n",
      "Nearest to of: vashem, in, hipparcos, including, beale, jackass, and, main,\n",
      "Nearest to there: they, it, he, she, now, often, still, who,\n",
      "Nearest to seven: six, four, eight, nine, five, three, zero, two,\n",
      "Nearest to were: are, was, have, be, been, had, those, civitas,\n",
      "Nearest to world: tinymud, shack, unfairly, meo, parnell, civil, entered, revolutionary,\n",
      "Nearest to or: and, than, but, carloman, oas, physique, though, savile,\n",
      "Nearest to other: various, different, dagger, scholars, many, those, these, several,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step 52000: 3.436360\n",
      "Average loss at step 54000: 3.425613\n",
      "Average loss at step 56000: 3.438066\n",
      "Average loss at step 58000: 3.396127\n",
      "Average loss at step 60000: 3.400386\n",
      "Nearest to during: after, although, in, at, before, when, under, melee,\n",
      "Nearest to history: list, development, economy, politics, iterative, holiday, trustedbsd, batll,\n",
      "Nearest to by: through, nongovernmental, satisfies, under, shutter, trains, distributes, excluding,\n",
      "Nearest to its: their, his, the, her, departmental, seeking, marcion, setback,\n",
      "Nearest to first: last, second, next, earliest, same, best, fourth, final,\n",
      "Nearest to with: between, rammstein, after, toward, in, when, including, valign,\n",
      "Nearest to their: its, his, her, the, your, our, my, whose,\n",
      "Nearest to which: that, this, what, who, it, however, both, maoist,\n",
      "Nearest to not: still, never, infections, who, almost, only, now, you,\n",
      "Nearest to of: in, including, for, although, jackass, coffey, timid, weddings,\n",
      "Nearest to there: they, it, still, now, he, this, often, usually,\n",
      "Nearest to seven: eight, six, four, nine, five, three, zero, one,\n",
      "Nearest to were: are, was, have, had, those, be, although, been,\n",
      "Nearest to world: tinymud, shack, unfairly, editorial, colloquium, rear, kanembu, scalability,\n",
      "Nearest to or: and, than, amputation, but, interpreters, meir, ismaili, tarentum,\n",
      "Nearest to other: various, different, unaided, many, dagger, are, more, several,\n",
      "Average loss at step 62000: 3.247619\n",
      "Average loss at step 64000: 3.254300\n",
      "Average loss at step 66000: 3.409534\n",
      "Average loss at step 68000: 3.400410\n",
      "Average loss at step 70000: 3.362137\n",
      "Nearest to during: after, at, before, in, within, under, although, throughout,\n",
      "Nearest to history: list, development, period, iterative, batll, colonial, factor, education,\n",
      "Nearest to by: using, through, with, alexandre, in, couscous, coolant, clipped,\n",
      "Nearest to its: their, his, the, her, marcion, begs, ouster, reformist,\n",
      "Nearest to first: second, last, next, same, earliest, final, best, original,\n",
      "Nearest to with: between, by, plunges, praying, in, prds, learnt, stony,\n",
      "Nearest to their: its, his, her, the, your, our, my, some,\n",
      "Nearest to which: that, this, what, also, usually, these, however, still,\n",
      "Nearest to not: still, never, now, almost, generally, graphically, infections, usually,\n",
      "Nearest to of: including, include, timid, in, aq, and, jackass, incubation,\n",
      "Nearest to there: they, it, still, now, we, he, usually, generally,\n",
      "Nearest to seven: eight, six, four, nine, three, five, two, one,\n",
      "Nearest to were: are, have, was, had, be, been, these, including,\n",
      "Nearest to world: scalability, tinymud, shack, parnell, editorial, drifting, anachronism, colloquium,\n",
      "Nearest to or: and, than, any, while, a, amputation, parmenides, but,\n",
      "Nearest to other: various, different, many, unaided, others, downing, some, dagger,\n",
      "Average loss at step 72000: 3.376581\n",
      "Average loss at step 74000: 3.345321\n",
      "Average loss at step 76000: 3.320806\n",
      "Average loss at step 78000: 3.351935\n",
      "Average loss at step 80000: 3.380442\n",
      "Nearest to during: after, in, before, throughout, at, within, although, despite,\n",
      "Nearest to history: list, pron, development, iterative, batll, facts, factor, colonial,\n",
      "Nearest to by: through, alexandre, using, asahi, when, including, dalit, scholastic,\n",
      "Nearest to its: their, his, her, the, your, our, seeking, marcion,\n",
      "Nearest to first: second, last, next, final, third, same, earliest, aelia,\n",
      "Nearest to with: between, plunges, molasses, predecessor, when, into, or, managers,\n",
      "Nearest to their: its, his, her, our, your, my, themselves, these,\n",
      "Nearest to which: this, that, usually, what, also, these, argumentative, often,\n",
      "Nearest to not: still, never, generally, always, now, usually, often, almost,\n",
      "Nearest to of: including, in, original, illuminating, aq, iis, despite, sur,\n",
      "Nearest to there: they, it, he, still, she, usually, now, we,\n",
      "Nearest to seven: six, eight, four, five, nine, three, zero, two,\n",
      "Nearest to were: are, was, had, have, been, those, be, ment,\n",
      "Nearest to world: scalability, tinymud, vietnam, colloquium, shack, signatories, rideau, drifting,\n",
      "Nearest to or: and, than, but, trang, while, aunt, clogged, with,\n",
      "Nearest to other: various, different, western, others, drawbacks, unaided, dagger, including,\n",
      "Average loss at step 82000: 3.408099\n",
      "Average loss at step 84000: 3.413153\n",
      "Average loss at step 86000: 3.391501\n",
      "Average loss at step 88000: 3.353668\n",
      "Average loss at step 90000: 3.363542\n",
      "Nearest to during: after, in, before, within, at, until, through, under,\n",
      "Nearest to history: list, colonial, development, iterative, pron, classical, facts, kandahar,\n",
      "Nearest to by: through, under, with, from, without, nongovernmental, when, clipped,\n",
      "Nearest to its: their, his, her, the, reformist, my, your, mormonism,\n",
      "Nearest to first: last, second, original, earliest, next, oldest, final, same,\n",
      "Nearest to with: between, in, plunges, by, without, using, streetcar, from,\n",
      "Nearest to their: his, its, her, our, your, my, whose, the,\n",
      "Nearest to which: that, this, what, usually, also, but, these, actually,\n",
      "Nearest to not: still, now, almost, otherwise, never, generally, t, nor,\n",
      "Nearest to of: for, including, in, jackass, include, during, under, escher,\n",
      "Nearest to there: they, it, still, he, she, now, usually, we,\n",
      "Nearest to seven: eight, six, five, four, nine, three, two, one,\n",
      "Nearest to were: are, had, was, have, these, became, while, been,\n",
      "Nearest to world: shack, scalability, rideau, beauvoir, anachronism, satellites, colloquium, civil,\n",
      "Nearest to or: and, than, linguistique, but, while, ismaili, teens, including,\n",
      "Nearest to other: various, unaided, drawbacks, others, different, western, several, smaller,\n",
      "Average loss at step 92000: 3.400494\n",
      "Average loss at step 94000: 3.254460\n",
      "Average loss at step 96000: 3.359706\n",
      "Average loss at step 98000: 3.243260\n",
      "Average loss at step 100000: 3.357312\n",
      "Nearest to during: after, in, within, before, at, despite, throughout, through,\n",
      "Nearest to history: list, colonial, pron, encyclopedia, iterative, classical, economy, batll,\n",
      "Nearest to by: through, including, clipped, under, toast, when, using, hal,\n",
      "Nearest to its: their, his, the, her, your, comstock, our, lemy,\n",
      "Nearest to first: last, second, next, fourth, final, third, same, aelia,\n",
      "Nearest to with: between, in, hailstones, crassus, makes, bye, including, plunges,\n",
      "Nearest to their: his, its, her, your, our, the, whose, my,\n",
      "Nearest to which: that, this, what, these, usually, but, actually, however,\n",
      "Nearest to not: still, never, almost, actually, otherwise, now, t, they,\n",
      "Nearest to of: including, and, in, illuminating, original, from, texaco, cernan,\n",
      "Nearest to there: they, it, still, she, now, he, generally, we,\n",
      "Nearest to seven: eight, six, four, five, nine, three, two, zero,\n",
      "Nearest to were: are, was, these, have, had, include, been, those,\n",
      "Nearest to world: shack, anniversary, scalability, ok, vietnam, rideau, upcoming, anachronism,\n",
      "Nearest to or: and, than, oas, yzerman, mprp, com, impair, commissar,\n",
      "Nearest to other: various, others, different, including, drawbacks, western, carnivorous, older,\n"
     ]
    }
   ],
   "source": [
    "num_steps = 100001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  tf.global_variables_initializer().run()\n",
    "  print('Initialized')\n",
    "  average_loss = 0\n",
    "  for step in range(num_steps):\n",
    "    batch_data, batch_labels = generate_batch(\n",
    "      batch_size, num_skips, skip_window)\n",
    "    feed_dict = {train_dataset : batch_data, train_labels : batch_labels}\n",
    "    _, l = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "    average_loss += l\n",
    "    if step % 2000 == 0:\n",
    "      if step > 0:\n",
    "        average_loss = average_loss / 2000\n",
    "      # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "      print('Average loss at step %d: %f' % (step, average_loss))\n",
    "      average_loss = 0\n",
    "    # note that this is expensive (~20% slowdown if computed every 500 steps)\n",
    "    if step % 10000 == 0:\n",
    "      sim = similarity.eval()\n",
    "      for i in range(valid_size):\n",
    "        valid_word = reverse_dictionary[valid_examples[i]]\n",
    "        top_k = 8 # number of nearest neighbors\n",
    "        nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
    "        log = 'Nearest to %s:' % valid_word\n",
    "        for k in range(top_k):\n",
    "          close_word = reverse_dictionary[nearest[k]]\n",
    "          log = '%s %s,' % (log, close_word)\n",
    "        print(log)\n",
    "  final_embeddings = normalized_embeddings.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "jjJXYA_XzV79"
   },
   "outputs": [],
   "source": [
    "num_points = 400\n",
    "\n",
    "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')\n",
    "two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4763,
     "status": "ok",
     "timestamp": 1445965465525,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2f1ffade4c9f20de",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "o_e0D_UezcDe",
    "outputId": "df22e4a5-e8ec-4e5e-d384-c6cf37c68c34"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ErsHatWt55JFH8PLywsvLi44dO5KZmelaV5bfUie/AiVAp06daNOmDbt27cLX15ekpCRe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F27lkZGRsJLun379oJbYUVdK//7CIj9dyAXW22iFUlJSYJbEPnfQK5p06a8f/8+8/Pz2blz\n53IHcsXLDh8+nD///DOPHDmiMhDOysritWvXqrzulSElJUUY+B88eJC9e/dm8+bNmZOTw4yMDFpY\nWLB58+Zs06YN69evLwwAyhvIJSQksG3btsI1SUtLY1RU1Cvrl5/3Yaq890JDQ+np6Uk/Pz+2b9+e\nQ4YMIUn+9NNPbNSoEc3NzYV106ZNEwSKduzYIZR3c3Njr1692K5dOyYlJbF9+/YcPnw427Zty8GD\nB/PYsWN0dXVl27ZtGRERQVIxOeHs7ExbW1u6urry2rVrzM/PZ8uWLWloaEi5XM6dO3dy0KBBdHBw\nIKm4v4yNjdmiRQva2dnR0dGREomEenp6lEgkdHFxoZ6enoo4RXl9QFXzvGc0KSlJRZwnJCSEXl5e\nZfYrsbGxdHFxEe6dku6WMplMpf8q2V+9jSxfvpzz5s17obJV0b8+ayCndAvfvv1XqqmpU09PQh2d\nRhw3bjwnTpzIJ0+esEWLFuUeOzw8nPPmzaOpqanKxIRI9fMiAznRtVKk0oh5z0RqG+VJOxdPaK2u\nri7khVNTUxPywpHEmjVrEB0djejoaCQlJWHTph+hqfkJ6ta9UCmp5uHDh2P9+vWIi4vDvHnzVNyq\niuerYwn35DZt2iAqKgoSiQRz587Fvn37yv2N/v37Y8eOHQgJUQSu16tXT2U7WTrReHGBjIqgFAXR\n0fFCgwa21S5XXVL4QU1NDZ9//jkcHR3h5uYGCwuLZ+5b/H99fX107twZW7ZswcCBA2FjYwMXFxfB\nRe5VER8fL7jcLVq0CHPnzsXo0aNhbW2Nrl27olmzZnjwIB2pqXrIyclDenpGmccpLoxQMn1FQUHB\nKxEzqIgrbvHrFBMTg9WrVyMhIQGJiYk4c+YMAgMD0atXLyxbtgxbt27Fnj17EBcXh/j4eBw9ehTT\np08XRCqio6OxZs0aQWQkMTER06dPx9WrV3HlyhX88ssvOH36NJYtW4YvvvgCgOJ8nT59GpGRkVi4\ncCFmz54NDQ0NLFq0CP3790dUVBT8/f1V6jpx4kRYW1tjypQpSE5Ohq6uLuLi4mBjY4OMjAyEhYXh\nvffew9atW4W2PasPqEqSkpKgqWkKRaoHoKwkzyWflZI56JT8+edhnDsXLrzTQ0KOq2x3dXXF4cOH\nheXi/dWcOXOwePHiKmjR60P37t3x/fffC4IwlaUi1+55VCTcZeDA/jA2fhd79vwPyclX4OTUQShr\nZmaGXbt2CfvGxSkE127cuAEHBwcsXLgQhoaGuH37dmWbJ1LTVHbkV9V/EC1yrxU1OVMvIlJRKiLx\nXdLFRrnthx9+YO/evVlQUEBSMduclZXFffv20c3NTeXeVpbZvHkz69WrJ7g4KmctS1qRZDIZg4OD\nOXz4cI4YMYI5OTnCcbp378709HSSpa01vr6+gtgFqbA0rV69miRZWFhIU1NT+vv7C2kUyP8C8suy\n1Ny6dYsk2ahRo0q5o4mW95rhde9XK+KKq7Rmh4aG0sfHR1g/fvx4wZ2yuLV18uTJ3Lhxo7DfsGHD\neODAAYaGhqoIqCQlJbFt27Yq+ymtRzdu3KBcLidJ3r59m3369KG1tTUlEokgT1/cgkGSCxcuZJMm\nTZibm8tGjRqxTZs2XL58OZs2bcrGjRszPz+fLVq0ECxbEydOVLH8l2dJrmoqYpFTU1PjuXPnSCrE\nO5YsWUITExMmJiaSVJzvL7/8ktraDf91wUskEMs6dTT55ZdfkvyvX5k0aRI//PBDkqX7qz59+lRL\nG2sjVSECVVXPu9I11N/fn1KplGTp+7m4W2jxbTdv3qSvry9tbGxoZWUliD598MEHlEgklEgknDx5\ncqXbJvJy4AUscnVf8ThS5DVDOZOUk1N6JkmUtBV5Vfj6+uK7776DlZUV2rVrV67Ed3GU20aNGoWk\npCTY2tqCJAwNDbFv3z4YGBjAwMBA5b5WlvHw8ECDBg1gb28PLS0tdOvWDYsXL8aiRYvg6OgIQ0ND\nODo64t1334W3tze2bduGP/74A9nZ2ULyV6XYCqCw1kyfPh3q6urQ1NTEt99+i5iYGIwYMQIA4ODg\nIIgPqKuro0ePHti8ebOKOEdZlpqioiJoampi3bp1MDY2xpgxYyCRSGBnZ6diRSiPpk2bvpbP9f37\n95GUlARTU9PXov4v0q/WpjaqplGR4nliK8Wt03Xq1BGs48+CxawO9evXL/d4xS3v6urqwrHnzp0L\nb29v7NmzB8nJyfDy8irzd0xMTNCmTRtIpVJkZGTA19cXampqWLBgASZOnAg3Nzfo6+ujTp06AIAB\nAwZg3bp1sLOzw65duwRLsqGhITp06KAiqFGVKK3mgYFe0NAwQUFBcimrefv27bFu3TqMGDECVlZW\nmDx5MpycnODn54fCwkI4ODjAw8MDWlq/ITd3OQA/AIVQV9cThHuU/cqqVasQGBiIWbNmwdvbu1R/\n9TZQ3PKseFbjEBjohU6dvLF+/Xro6elhypQpzz1ORa5dRdi2bVupdQEBAQgICBCWb9y4UeY2U1NT\nFZEfJbt3765UHURqAZUd+VX1H0SL3GvF6z5zLCJSFSjjcgYPHkwLCwv6+/szOzubpqamnDlzJu3s\n7Lhjxw7BwrB69WpqampSKpUK1gTlTHdWVha7d+9OmUxGiUTCnTt3Ctvnz59PW1tbSqVSQR5a5NnU\nVNqEqqSy/WptbOPzxFaKx8iVl76muEVuz5499PX1ZWFhIVNTU2lqasp79+6VKl9WnGRZYjh9+vTh\nnj17SCriaZVpH3bv3s2AgAChfHGrhbGxMSdNmkSS3LhxIz/44INSv1G8bS9LTEwMDx06VKkyL2s1\nF9/pFedZlufnpakoi9rm8VDb6vM2AjFGTqS6qenYGRGR2srVq1cxYcIEJCQkoEGDBli/fj3U1NTQ\npEkTXLhwQSVNwIABA9C4cWPs2rVLkLVWznQfPnwYRkZGiI6ORlxcHHx9fYVyhoaGiIyMxLhx47Bs\n2bIXqmdNJPWuLdR02oSqojL9am1t4/PSqJRnHS8Z46ikT58+kEqlsLGxQadOnbBs2TIYGhpW6hjF\nmTFjBmbNmgU7OzsUFRUJ6728vJCQkABbW9tSMvpOTk4ICQmBTCZDUFCQIG//rDjN4s8by4ldKo+Y\nmBgcOnSoUmUaNWr0UnGRlX2nv039SUlULc8AMBEZGbGYNGnSM9NUZGZmwtzcHIWFhQCAjIwMmJub\nv/S1q0pE7YPXmMqO/Kr6D6JF7rVEnLkReZspSxGud+/eNDMzE+LRSMXM/SefTKGOTiOqqWlSW9tA\nsFQoLXLlKcCZmpoyJSWFJHn+/Hl27ty50vWsjZab6qQm0yZUBxXpV1/3NtZmNm/eLCQ3HzZsGEeM\nGMFJkybRxcWFrVq1EqxwN2/epIODA21sbCiVSrl//36S5KpVa6impk4NjUZUU6vDNWvWcvz48XRw\ncKC1tTUXLFgg/FZ4eDhdXFxoY2PDDh06MD09vZSCZlZWFkeOHMkOHTrQ1taWv//+O0mF1bBXr170\n9vamp6dnlbS9Ivfe29aflIXyHNSv345qanW4efPWCqWpGDlypHCf/PDDD5w2bdorqX9ZiFbZ2gPE\n9AMiIiIi1U9SUhJNTU2F5ZCQEPbp06dUvqEBAwZQU1P33xekKYGTwgtSOZAj/8sv5uHhIQSdF99+\n4cIFenl5VaqOb+PL+U1tc2hoKM+cOUPyzW3jq+bSpUts166d8CH+6NGjMvM/bt/+K7W1G7JBAxvq\n6DTi99//yNatWzM1NZVaWvoE6hAIF67L9evXSSpEijw9PRkfH8/8/Hyam5sLYkYZGRl8+vRpKaGK\nTz/9VBCCefz4Mdu2bcvs7Gxu2rSJxsbGfPz4cY2dH/G++4/U1FROmTJFZTA2ZcoUrlixotz0E2Fh\nYezduzdJ0tnZmZcuXXoldS8LcXKo9vAiAznRtVJERETkBUhOTsb58+cBANu3b4ebm1upfTIzM1G3\nblMoBCAaADAuJTP9zz//QEdHB4MGDcL06dMRFRVVJfWrConr143a5vqtdKUqDivpbgcAoaGhOHPm\nDIDa18Y3hZCQEPj7+6Nhw4YA/pPq7927NwCFiNC9e/cQGPghcnOP4ckTN+TkNMG4ceOQkpKCqKgo\naGq+C8AEgAOUz9tPP/0EOzs7yOVyJCQkICEhAVevXkWLFi1ga2sLANDV1RXEU4pz5MgRLF26FHK5\nHJ6ensjPz8etW7cAAJ07d4a+vn61nxclb2N/Uh5NmzZFy5YtS4nukCw3/YSLiwuSkpJw4sQJFBUV\nwdLS8lVUvUxKu4w+W6xIpHYhDuREREREXgClIpylpSXS09MFVcni6Onp4enT+1C8IEcD8EJmZjxM\nTU2FuJqy8osBz1bcrAhv68v5ebFaL8qWLVtgY2MDuVyOgIAAjBgxAnv27BG26+npAVDkNHR3d8f7\n778PKysrJCcno3379ggICIBEIsH//d//4ejRo3BxcYG9vT369++P7OxsAICZmRkWLFgAOzs72NjY\n4Nq1a0hOTsZ3332HlStXwtbWFmFhYdXWRpHSFFfEVKjAmgKIB/AAwGXo6Umhr68PQ0ND5Of/HwDl\ngCwOeXk3sHPnThw/fhyxsbHo1q2b8GFf0QH97t27hRyXN2/eRLt27QCUVu6sbt7W/qQ83N3dsW/f\nPuTl5SEjIwMHDhwAoJi8a968OQoKChAUFKRSZujQoRg0aBBGjhz5KqpcLuLk0GtOZU14Vf0H0bVS\nRETkDeZ5an7lURVxqC/62yKqlOd2V1y5sHieNF1dXSYnJ5NUuOHWqVNHcFN68OAB3d3dhRyEX331\nlYo77bp160iS69ev5+jRo0mWzoEoUvUor7HSnTktLa3UNa5fv/6/7oUzCEwiEEtNTT2qqakxOTn5\n3xi5OsLztnTpV5TJZCwqKuLdu3fZrFkzbt68mfn5+WzVqhUvXLhA8j/XypIKmp999hknTJggLEdH\nR5MsnSusphD7E1WWLFnCtm3b0s3NjYMHD+aKFSv43Xff0czMjB06dOCkSZNU8gjevXuX9erVE/KH\n1jZE7YNXD8Q8ciIiIiK1i4ED+6NTJ+9K5fz65ZcdCAz8EJqailnwDRvWv5DV5UV+W6Q05bndlYej\noyNatmwpLJuYmMDBwQEAcO7cOSQkJMDV1RUkUVBQIOQ9BBRqjQBgZ2eHvXv3VnVTRMrB0tISn332\nGTw8PFC3bl3I5fJSVnF1dXX8+ON6jBw5DgUFBSgqWgcXFzekpqYCAN5/vye++249Nm/+Xnjerly5\nDAsLCxgbG6Njx44AAA0NDezYsQMTJkxATk4O6tWrh2PHjsHLywtLly6Fra0tZs+ejblz5+Ljjz+G\nVCpFUVERzM3N8fvvv9f4uVEi9ieqzJ49G7Nnzy61fuzYsWXuf+rUKfj5+aFBgwbVXbUX4nXNG/q2\nIw7kRERERKqZyrwgn5V09kVesuLLuXqoW7euIGNPEvn5+cK2km5vxZdJwsfHp5TblRKlK19FE2WL\nVB1Dhw7F0KFDy93+5MkTAHjmYCYhIUFleePGjWUey87ODmfPnlVZV69ePYSHh6us++6770qVLZn0\nuSYR+5MXY/To0fjrr79KpbgQEXlZxBg5EYH//e9/kEgkkEqlWLVqFZKTk2FpaYkxY8bA2toavr6+\nyMvLAwDcuHEDXbt2hYODAzw8PHDt2rVXXPuKk5ycDIlE8qqrISJSJqKoQO3D29sbv/32G9LS0gAA\njx49gqmpKS5cuAAA2L9/PwoKCsotz2LxUE5OTggLC0NiYiIAIDs7G9evX3/m7+vp6QmDCJFXT9Om\nTV9J/q/amsPNzMxMeDZESvPLLzsQFLQHT540hZdXNzFHm0iVIg7kRAAAUVFR2Lx5MyIiInD27Fn8\n9NNPePToEa5fv46JEyfi4sWL0NfXx+7duwEAY8aMwdq1axEREYFly5Zh/Pjxr7gFleNlhSRERJSs\nXr0alpaWz5zJrwylRQUWIysr4a0VFagNFHe7k8vlmDp1KsaMGYMTJ05ALpfj3LlzzxSfKN7fNGnS\nBJs2bcLAgQNhY2MDFxcXIZlwef1Sz549sXfvXkHsROTto7YmbC4qKhLfp8+guIdFenokcnKOIzDw\nw1o3GBd5fVErPlP4SiqgpsZXXQcRxcdoWloaFixYAACYP38+mjRpgrVr1wofGV9//TWePn2Kjz/+\nGE2bNkX79u2FmeaCggJcvHjxVVW/UiQnJ6Nnz56Ii4vDjRs34Ofnh0GDBiEsLAxZWVn4+++/MXXq\nVOTn52Pr1q3Q1tbGoUOHnhsXI/J2YmFhgeDgYLRo0UJYV1hYWKaceEVRxshpaJggJ+cavL3dcPjw\nn1VRXRERkdeM+/fvw8SkPXJyjkNhqY+Djo4XkpOvvJRVcPny5dDW1saECRMwefJkxMXFITg4GMeP\nH8eGDRvQo0cPLFmyBADQrVs3LF26FIDCQjx27FgEBwdj7dq1GDp0KC5cuAAdHR307dsXffv2xcCB\nA9GvXz/cuXMHhYWFmDt3Lvz9/avgbLxeREREoHPncUhPjxTWNWhgi2PHvhfiZkVElKipqYFkpWZG\nRIucSJkoB2jFpZeVMRtFRUVo2LAhoqKiBFnk12UQV5xr167Bz88PW7ZsQdOmTXHp0iXs27cP4eHh\n+Oyzz6Crq4uoqCg4OTlhy5Ytr7q6IrWQ8ePH4+bNm/D19YWBgQGGDRuGjh07YtiwYSgqKsKMGTPQ\noUMHyGQy/PjjjwAU8vReXl7w9/eHhYWFiiUvIiICrq6u+OqrL2FpaYYDB1ZixYovUb9+PXTt2hXt\n2rXDzJkzX1VzRWqY2upKJ1KzVJe7tZubG06dOgUAiIyMRFZWFgoLC3Hq1Cm0bdsWs2bNQmhoKGJi\nYhARESEIrWRlZWHLli0IDg6Gq6srACAjIwO9evXC4MGDERgYiMOHD8PIyAjR0dGIi4uDr6/vS9X1\ndUVM2yBS3YgDOREAig593759yM3NRVZWFvbt2wd3d/cyc93o6enBzMwMu3btEtbFxcWV2q82k5qa\nit69e2P79u2wtlivnpEAACAASURBVLYGAHh5eaFevXpo0qQJDAwM0KNHDwCARCIR45NEyuTbb79F\nixYtEBoaismTJ+Py5csICQlBUFAQNmzYAAMDA5w/fx7h4eH44YcfkJycDACIiYnB6tWrkZCQgMTE\nRJw5cwYFBQUYMGAA1qxZg5iYGISGhsLV1RUNGjRAbGwsfvvtN8TFxWHHjh24c+fOK265SHVTW13p\nRGqe6hoM2NnZITIyEhkZGdDS0oKzszMiIiJw6tQpNGzYEJ6enmjUqBHU1dUxePBgnDx5EoBiUrek\ngE/v3r0xcuRIDB48GIDivXn06FHMnj0bp0+fFvIsvm2IOdpEqhtxICcCAJDL5Rg+fDgcHBzg7OyM\n0aNHw8DAoFzf923btmHDhg2QyWSwtrbGzp078e233wJQWBx69uxZk9WvNPr6+mjZsqUwGwmoWh/V\n1NSEZXV1dVE9TqRC9OrVC5qamgCAI0eOYMuWLZDL5ejQoQPS0tIEUQtHR0e88847UFNTg0wmQ1JS\nEq5evYoWLVrA1tYWAKCrqyu4Z7733nvQ1dWFlpYWLC0thQGhyJtJTcXViMJPrwdVORhYvnw51q5d\nCwCYPn060tLSsGnTJrRo0QKRkZFYuXIlTpw4gRUrViAy8j93wA8//BChoaGQy+XQ1NQUvg1ycnJw\n7949NGzYEIcPHxb2b9OmDaKioiCRSDBnzhwsXrz4Jc/C68vAgf2RnHwFx459j+TkKy+USkZEpDzE\ngZyIwCeffIL4+HjExcVh4sSJMDExUbG0TZ06FfPmzQOgmCH8888/ERMTg4sXL2LUqFFYv349AMXs\nXEWCn5XS3a8CLS0t7N27F1u2bMEvv/zyyuoh8mZRcpZ6zZo1gvtxYmIiOnXqBKBsl2VlmbIob3+R\nN5OaVC6tTUIVLzOwfB0mEF+GqhoMlHSnrFevHpYvXw41NTU4OTlh165d6NKlC86cOYO///4bQUFB\nKCwsRF5eHjp37ozo6Gioqys+HZXulLq6uti1axcMDAzw0UcfAQD++ecf6OjoYNCgQZg+fTqioqKq\n5kS8prwqpVORNx9xICdSYdLT0wWrW8nYjdmzZ+PGjRuwtbXFzJkzkZGRUWYMkJmZGWbNmgV7e3vs\n2rWr3DQGDx48gJ+fHzp06IAOHTrgzJkzVd4eHR0dHDx4ECtXrkRGRobKttr0cSNSuylv8NWlSxes\nX79eGHRdv34d2dnZ5ZZp164d7t69K8yCZ2ZmorCwsJpqLVKbqcm4mqdPn5ZKMRMTEwNnZ2fIZDL0\n7dsX6enpABTu51OmTIGDgwOsrKxw4cIF9O3bF+3atcPcuXOFYwYFBaFDhw6wtbXF+PHjy31GyuJl\n+t6XKZucnPxCk3o1adWsisFASXdKJycn/PPPP7h16xaMjY1Rv359dOrUCS1atEBAQAA++eQTITm6\nUvjkX0EGwZ1SV1cXALBq1Srk5uZi1qxZiI+Ph6OjI+RyORYtWoQ5c+ZUyTkQEREpAclX+qeogsjr\nwM2bN2ltbc3t23+ljk4j6uvbUkenEbdv/5VJSUmUSCQkydDQUBoYGDAlJYVFRUV0dnZmWFgYSdLU\n1JTLli0Tjvnee+/x77//JkmeP3+e3t7eJMlBgwYJZW7dukULC4uabKqISIVYtGgRNTQ06OzsTGtr\na/bq1YuJiYn09fWlvb09jY2N2bZtW1pbW7N58+YMDAykpaUlzc3NuWDBAgYEBLBFixZs0qQJ9+zZ\nw2HDhrFevXrU09Ojk5MTs7Ky2KdPHzZr1owSiYRjx45lz549eeLECXp6enLmzJl0dHRku3btePr0\n6Vd9Ol4rkpKSaG1t/VLHCA0N5ZkzZ6qoRqoo+9kGDeRCP1vVJCUlsW7duoyLiyNJ9u/fn9u2baNU\nKuWpU6dIkvPmzePkyZNJkp6enpw1axZJctWqVWzRogXv3bvHvLw8vvvuu0xLS+Ply5fZs2dPPn36\nlCT54YcfcuvWrRWuT/v27Tl48GBaWFjQ39+fOTk5PHbsGOVyOaVSKQMDA5mfn0+S/PPPP9m+fXva\n2dlx0qRJ7NmzJ4uKitimTRs+ePCAJFlUVMTWrVsLy+Vx/Phx9ujRo8xtyraUV2flu+914b333uPq\n1as5f/587t69m0uWLKGZmRl///13Dhs2TNhvw4YNnDp1KklST09P5Rimpqb86KOPVPYvTmpqKsPD\nw5mamlp9DRERecP4d0xUuXFUZQtU9Z84kHt9GDBgAOvVq0c1tToE3idwgEAs1dU1+P7771MikfDn\nn3/m0KFD6ePjwxUrVtDa2pqNGjXi0KFDSSo6/1u3bpEkMzMzqaOjQ7lcTplMRplMRisrK5KkoaGh\nynpjY2NmZWXVWFvFl5DI84iIiKBcLmd+fj4zMjLYpk0brlixotzJieHDh7Nnz55C+QULFtDNzY2F\nhYWMjY1lvXr1+Ndff5Ek+/Tpw/3795Mkr1+/LtyLQ4cO5cGDB0kqPqqnTZtGkjx06BA7depUY21/\nE6iKD/AFCxZw+fLlVVSj0lR3P5SUlMS2bdsKy1999RUXLlxIExMTYV1iYiLt7OxIKu455cA1JCSE\nPj4+wn4eHh6MjY3l2rVraWRkJPTf7du358KFCytcHzU1NZ49e5YkGRgYyMWLF9PY2Fh4poYNG8ZV\nq1YxNzeXxsbGTExMJEk6OjpST09PeGfMmzeP3t7eNDc3p6GhIW/fvk1S8RxOmjSJLi4ubNWqFXfv\n3k2SdHJyooGBAeVyOVeuXMlNmzaxV69e9Pb2pqenJ0ly2rRptLa2plQq5Y4dO4Q6v24DuQULFrBl\ny5YMDg7mvXv32LJlS37wwQf8559/aGpqyocPH/Lp06fs1KkTDxw4QJLU1dVVOYZyv0mTJvHDDz9U\n2VbWZK+IiMjzeZGBnOhaKVJhli5dihYtWqBBAxsAAwGcgiKGQwNXrlwBAJw6dQo2NjbIzc0VEoz7\n+/sjJCQEsbGxAP6LI3pWGgOSOH/+vLD+1q1bqFevXo20U1SLE6kIYWFheP/996GhoQFdXV306tUL\nOTk5OHPmDPz9/SGXyzF27Fjcu3dPKFMyj1LXrl2hrq4OiUSCoqIi+Pj4APhPKfWXX3bAykoOZ2dP\nNGv2Dv744w9cunRJKP/BBx8AULhLiQIolaegoABDhgyBpaUl+vXrh9zcXERFRcHT0xMODg7o2rWr\ncP1Wr14NKysryGQyDBo0CMnJyfjuu++wcuXKakvUXRNxNSXjLx8/flyh/dXV1UsJRD19+hQkERAQ\nIPTrly9fFmKrK0LLli3h5OQEABg8eDCCg4Nhbm6OVq1aAQACAgJw8uRJXLlyBebm5jA3N0dCQgLu\n3LkDV1dXREdHY+vWrVi1ahVGjBgBR0dHDBo0CBMnThR+4+7duwgLC8OBAweEdB5Lly6Fm5sboqKi\n8PHHHwMAoqOjsWfPHhw/fhx79uxBXFwc4uPjcfToUUyfPl3l2X6dcHNzw927d+Hs7AxDQ0Po6OjA\n3d0dzZs3x9KlS+Hp6Qm5XA57e3tBvbmk26pyubg7JSAmwBYRqWnqvuoKiLxeaGho/Bu70RjASQD7\noKb2FO+++y6uXbuGs2fPwt/fH5s2bULfvn2hra0NDQ0N2NnZqShEAqppDPz8/AAo0hhIpVL4+Phg\n1apVmDZtGgAgNjYWNjY21d6+4i+hnBxF4tXAQC906uQtBilXMXp6eqViE4uTnp6O7du3Y/z48TVY\nqxeHpMrkRFkUF0MB/vsoVlNTg4aGhrBeXV0djx8/xsyZC5CfrwVFrNQDPHnihIcPH5YqLwqgvBhX\nr17Fxo0b4eTkhFGjRmHt2rXYu3cvfv/9dzRu3Bg7d+7Ep59+ig0bNuCrr75CUlISNDQ08OTJEzRo\n0ADjxo2Dnp4epkyZ8qqb8sKwRPyavr4+GjZsiLCwMLi6umLr1q3w8PCo8PHee+899O7dG5988gma\nNm2KR48eISMjAy1btqxQ+ZIDBgMDA6SlpSE5ORm+vr4wMjJCeHg4njx5gsLCQgQHB2P48OHIzMxE\nQkICCgoKkJubiydPnqB58+YIDQ3Fo0eP0KBBA+Tl5WHXrl344YcfcOPGDUyZMgU3b96Eh4cHRo8e\nDQAYMWIEtLW1ceTIEejr60NfXx8AcPr0aQwcOBAAYGhoCE9PT0RERLyWqp/e3t7Iy8sTlpUTsQDQ\nv39/9O9fWkjlyZMnKsvnz59HYmIiCgsLsWHDBmG9UqhH8f4Eigv1VOQdamZmhsjISDRq1KiSrRIR\neTsRLXIilaJu3br/yiD3h7p6NDQ0BmPUqJHo1q0bDA0NkZycjAULFqiUKf5iLvmSVubbUqYxUCYc\nXbVqFS5cuAAbGxtYW1vj+++/r/a2ATWrFve28zxhgkePHglKqLURV1dXHDhwAHl5ecjMzMTBgwdR\nv379F86xWPKDOi0tDZqaLQFoQDFxYo6iIpZrMSlZvrLb30ZKWn/++usvXLp0CZ07d4ZcLscXX3yB\nlJQUAICNjQ0GDRqEoKAgIS3Em0BZlpbNmzdj2rRpkMlkiI2NFSxqz3pmldssLCywePFi+Pj4wMbG\nBj4+Prh7926F65OcnIzz588DALZv3w4HBwckJSXh1q1buHr1KrS1tbFkyRIYGRnh4sWLGDp0KEaO\nHIl3330XRUVF+PbbbyGXywEorHcmJiaQSqUoKCjA+fPnYWhoCC0tLYwZMwZr165FvXr1sGzZMvzv\nf/8T6nDnzh3MmzcPXl5e5dbzbX6enuW18rJCPaLQmIhI5RAHciIVRmlBUcogd+vWGc2aNcKMGdPR\nsWNHpKamYvz48Th//jy2bNkiJBj/8ssvcePGDbi5ueHGjRsqM20mJiYqaQyUylZFRUWYOnUqjh07\nhosXL9bYB31NqsWJKMjKykKnTp1gb28PGxsbHDhwAEBpJVRAkQPJ0dERMpkMCxcufJXVhr29PXr1\n6gUbGxt0794dUqkU+vr6pSYntmzZAolEAjU1NSQmJuKTTz4p83hqamoqEuqNGjVCQcFtAD0BWAFw\nh5paEQwMDIT9S5YvTnJyMtq3b4+AgABIJBJs3boVUqkUUqlUcIMCFM/1jBkzYG1tDR8fH0RERMDL\nywutW7fGwYMHhWO5u7vD3t4e9vb2OHfuHACF5LuXl1eZCrW1gTVr1sDS0hKNGzfG119/DQBYuHCh\n8NFe8pzp6enByspKcAuMjY3Fn3/+CQD4448/MGHCBERFRcHBweGVpk+pKspLMSOVSnH27FnExMRg\nz549glUqJCREyHPo4eEhTLwV33b//n2YmpriyJEjiI2NRUREBBwdHStcp/bt22PdunWwtLTE48eP\nMXnyZGzcuBHjx49H3bp10bx5c4wdOxbDhg2DiYkJ0tPTsXv3bqSkpOCdd97ByZMnkZ6ejsaNGyMt\nLQ2FhYWws7ODkZERTp06hWbNmiE3N1dwgc7KysLYsWORnp4ueAiUdIEGFO6IO3bsQFFREe7fv49T\np04J7XqbBnXPc52sTM677Oxs9OjRA3K5HFKpFDt37gRJrF69GnZ2drCxsRGUrB89eoQ+ffrAxsYG\nLi4uQhiGVCoVrIVNmjTBtm3bACgG8cHBwTVxSkREXi2VDaqr6j+IYievFYMHD6ZEIuGMGTO4YcMG\nGhkZkSQLCgqoq6vLffv2Cft+8803tLa2pkQi4erVqyv8G686ULom1OLeJlauXMmcnJxS65UqaE+f\nPmVGRgZJ8sGDB2zdujXJ0iICR44c4ZgxY0gqlOh69OghKOu9KjIzM0mS2dnZtLe3Z3R0dKl9KiOG\nEBoaqiKI8qx78XlCGElJSaxTpw7Dw8OZkpLCli1b8uHDhywsLKS3t7cgpqKmpqYistKlSxdBgEUm\nkwnty8vLI6kQX7G3txfqW55CbW2gffv2vHPnjsq6BQsWcMWKFYKwxrlz50iSo0aN4tdff802bdoI\nYhsFBQW8dOkSScX5JMn8/HwaGRkxPT2dK1as4Pz582uuQbWc6uy7k5KSVERYQkJC2KdPH3p4eJAk\nt2zZQjMzM+rr63PEiBEcMmQIdXR0qKurSw8PD3p7e7Nr167s3bs3t23bxhYtWpD8rx8qKCigt7c3\nGzVqxBEjRnDz5s2cOHGiSh1mzJghiJ389ttvQr1eN7GTlyE8PJz6+rYEKPw1aCBneHi4yn4VEerZ\nvXu30KeTZHp6Ok1NTblu3TqS5Pr16zl69GiS5MSJE7lo0SKSimuv7JvGjx/PQ4cO8eLFi3R0dBSO\n16ZNG2ZnZ1ddw0VEagCIqpUirzupqanU0WlEIPbfl0QsdXQa1bh6pKhaWXUo1c1KUvwDasKECZRK\npZTJZKxXrx7v3btX6gNp2rRpNDMzE9Tw2rRpw59//rla675t2zY6OjpSLpdz3LhxLCwspK6uLj/7\n7DPa2NiwSZMmtLa2poWFBWfMmEEnJydKpVLOmTNHUHkrmZpDKXEeGhpKmUxGuVxOW1tbZmZmMjQ0\nlJ6envTz82P79u05ZMiQMu/FinwwJyUl0dzcnCS5f/9+BgQECNuKy4praWkJ6+fNm8clS5aQVAyW\nGzZsSFLxgTV06FBKJBLKZDLWr19faENx5cLx48czKCjo5U56FTFu3DhqampSKpXym2++4YQJE0iq\nDuTq1atHCwsLamtrU09Pj6dPn+Z7771HHR0dNmvWjNbW1vzpp59YUFDAjh07UiqVUiKR8OuvvyZJ\nXrt2jVKplHK5/K1P/1DdfXdZA+8lS5bQxMREUK4cPnw4lyxZwo8++oiGhoZs1qwZ582bR1KhSql8\nHkjS1dVVGIyRZGxsrHAMpZLls9r6tr4fqvI6X7t2jWZmZpw1a5YwKWdqasqUlBSSCtXfzp07kyTl\ncjlv3rwplG3ZsiUzMjIYFBTEmTNncv369fz555/p7OzMO3fusEOHDi/fWBGRGuZFBnKi2IlItXH/\n/n0kJSXB1NS0wkIhLxsoXVU0bdpUFDd5AbKzs9GvXz/cuXMHhYWF8PPzQ0pKCry8vNCkSZMyXV2C\ngoLw4MEDREdHQ11dHXp6eti7dy98fX1V9iOJ2bNnC6IE1c2VK1ewY8cOnDlzBnXq1MFHH32EoKAg\nZGdnw8XFBYsXL8bMmTOhr6+PTz/9FD179sTkyZPRr18/fP/99+XGeijXr1ixAuvXr4ezszOys7Oh\nra0NAIiJiUFCQgKaN28OV1dXXL9+HS4uLkL5ygjyKMVVQkJCBHfIkpQUWSkuwKIUUPnmm2/QvHlz\nxMXFobCwEDo6OkKZkqqHtUV05dtvv8Vff/2F0NBQ/P7776Wuh4mJCRwdHeHk5IQvv/wSq1evRr9+\n/RAdHQ0DAwO0atUKJ0+eRMOGDQGglFgTALRp00ZQ433bqYm+u127dli3bh1GjBgBKysrTJ48GU5O\nTvDz80NhYSEaNWqMX3/dDy0tM+TlFaCwMBPu7u6K2kilSE1NFY4VFBSEcePGYfHixXj69CkGDBgA\nqVT63BitX37ZgcDAD6GpqXDD37BhPQYOLC0O8qaidJ0MDPSChoYJCgqSy3WdfB5t2rRBVFQUDh06\nhLlz58Lb2xtqamoVEnHiv+6s7u7uWLduHW7fvo0vvvgCe/fuxa5du+Dm5vbijRQReY0QB3Ii1cKL\nvuxUY9QUH6lijNrrw+HDh2FkZCTEVj158gSbNm1CaGio8EGsRPkiTk9Ph6GhIdTV1REcHIzMzEwA\npVUtu3Tpgnnz5mHQoEGoX78+UlJSoKGhUW0D7uDgYCEeiiRyc3PRrFkzaGpqolu3bgAUsv/Hjh0D\nAJw9exb79+8H8P/snXl4TVf3xz83EoQMFTW0WhKlCZKb3ExIkMQQQ82aKqVEUGNVq4YWRfHr+6I1\nlbaqaBEhVEvVW4IipkwSxFhyaU0hhEQSGdbvj/SeJjIQEkn0fJ7nPsm5Z5999tn33HP32mvt9YW+\nffvy4YcfFlq/p6cnY8eO5a233qJnz57UqVMHAHd3d1544QUAnJyciIuLy2XI/fHHH488YDb0cf36\n9bl8+TIJCQlYWloSGBiopFgvjJyf0csvvwzA999/T2Zm5kOPLSsYrqEgunbtCmRLPtjb21OzZk0A\nXnnlFS5dupTnvn2cCap/A0/j2W1sbMz333+f6z0fHx8iIyOJj4+nXj07UlP3kJqafX5TUx+02uzv\nyYMJswzrsx/ku+++K/D8albjbPr06U3btq2f+Htw5coVrKys6Nu3L5aWlnz77bcFlm3ZsiWrV69m\n8uTJ7Nmzhxo1amBmZoaZmRk3btwgPT0da2trWrRowdy5c/nyyy8f9/JUVMoVarITlWJn9uzZvPVW\nX1JSmhdZR2bdunVYWZkCTlSqVKfQhdIqZQ8HBwd27NhB165dady4Md7e3ty4cYPMzExGjBiBu7s7\nDg4OTJ8+XZn5njt3LsHBwVSpUoVZs2YpyTyio6PJyMhAq9UyYcIENBoN9+7do3nz5mi1Wvz8/BSj\nrySQAvSwcnqwcs4Y55zJf5jxADBhwgSWL19OSkoKnp6eyqJ+Q5KSVq1asXPnTrZs2YKPjw9jx47F\nzc2NHTt2kJZ2HmgHNAUcSE09h7W1Nffu3SMgIIBmzZrh5OSk6FxZWlri6emJt7c3r7zyCidPnlSM\nQ41GQ4sWLfJto+GajIyMWLlyJTqdjjNnzjzU21ieeJguWk5KSmMyIiJCSYLz+++/c/DgwSLXYWNj\nQ0JCQrG053EoSpKLx6Ww++tJMw7Hx8cTFhZW6O+UmtX4H4pD4/DYsWO4u7uj0+mYMWMGU6ZMKbDs\ntGnTiIiIwNHRkY8++ohVq1Yp+5o1a4atrS2QbfBdvny5wGeaisozR1FjMYv7hbpG7pnD2tpazM3t\nH7oYOj8MyQmuX78uhw8f/leuQSjvHD58WHQ6nXh5ecmMGTPEwsJCli5dKrdu3RIRkczMTPH29pZj\nx46JSPb9MmfOHOX4nGtUGjVqJDdu3BARkZ49e8rnn3/+1O6J2NhYefXVV5XzJSQkiF6vV9a+iYgE\nBweLv7+/iIh07txZgoKCRETk66+/VtYAPrhGzpDMxLCuR0Tk9ddfl59++km++uorsbCwkPv378vd\nu3fF0tJS+vTpI97e3jJy5EilvIeHp1SsaC4WFjqpVMlSSTr00UcfKWvU+vbtKy+88ILcu3dPVq5c\nKaNGjZIff/xRWrVqJYmJiSKSnWjmUch5zeUJw/rMlStXKokrDGvkRES8vb0lIiJCRPImmsm5T+Tp\nrd+dNm2azJ07t8jH2djY5LsW9WlTWuvHnuTzedQkLWVlDXdJsnLlSrly5YqyXdAaZxUVleKHx1gj\np3rkVIqV4cOHc/nyZZKSTgLjgB6ALUlJx0hJSQFyp/+GbC/OxYsX6devH6dOncLe3h5HR0eWLFlC\npUqVcoXm3Lt3j7p165KZmcn58+fp2LEjbm5ueHl5KR4NldLjypUrHDhwgOvXr3Pp0iXmzZtHWloa\np0+fJigoCBcXF3Q6HbGxscTGxirH5SdAC9C/f39Wr17N8uUr+PHHzUyb9kOxekIKIz89rCtXrhTo\nFfjiiy/4/PPPcXJy4o8//lBSthfE/PnzcXBwwNHRkYoVK9KxY0eOHz9O7dq1MTExwczMjPr16yMi\naDSaXH107txZbG2tqV07mYYNX8bIyIhly5bxxRdfEBAQgJWVFdu3bycpKQlPT0/Gjx/P5s2b+e9/\n/8ukSZPo3Lkz3bp1o0mTJkB2GCvA1atX8fLywtnZGa1WS2hoKJMmTSIlJQVnZ2dFXsBQPi4uTpGD\ncHR0VNLR6/V6GjduzNChQ7G3t6dDhw65BIifFg/zED6KLpqBonhj9Hp9LqHoefPmMX36dHx8fJg4\ncSJNmzbFzs6O0NBQAEV2Qq/X89VXXzF//nycnZ0JDQ3lxo0bvP766zRt2pSmTZty4MABIFtnsH37\n9jg4ODBkyJBH8gI/DYrDU/O4530cj+DD0ukXxznKC1lZWaxcuZK//vpLea+8eNkfxaOqovJMUlTL\nr7hfqB65Zw4bGxv55ptvpUKFSlKp0gtiamolH388RUkXnHNGXETEwcFB9Hq9xMXFCSA7d+7MNYPe\nvXt32bNnj4iIBAUFKemI27RpI+fOnRORbC9Q69atn+ZlquTD//73P3nxxRelVq1a4u7uLhEREbJ4\n8WKpX7++mJqaKp6ggQMHyqpVq0Qk74xvTo/c5cuXxdHRUUxMqgj4l+lZ8JyprtetWyfdu3cvch3z\n58+XadOmKdvvv/++zJ07V3x8fHJ5h2rUqCH3799Xtk+cOCG2trbi5OQkZ86ckVu3bsnAgQPljTfe\nEBGR2bNnS9WqVcXe3l6+/vprMTMzE71erxxv8B7OmzcvV9ZKg7yCYX/O8mvXrpPKlauJhYWjmJpa\nyddfL8slHWFiYiIxMTEiIvLGG2+UmWyW+TFkyBA5efJkoWWK4o15MOPq3LlzZdq0aeLt7S3jxo0T\nEZFt27ZJ27ZtRSS3N/DB52Pfvn0VSYeLFy9Ko0aNRETk3XfflU8//VRERH755RcxMjIqFs/J0aNH\nZdu2bU9cT2lRVI/go6bTf5JzPG3yy7Y7fPhwcXNzE3t7+1zPGGtra5kwYYK4uLjI6tWrxczMTOzs\n7ESn00lKSopYW1vLJ598Is7OzqLVauX06dOleGX5U9qSRSoqxQWqR06lrNCzZ3caN36VoKCl6PWn\nmDlzBgkJCfmuaZIcM8nGxsbodLpc+9944w2CgrI9MOvWraN3794kJycrgq46nY533nlHWQ+kUnr4\n+vqyc+dOLC0t2bp1K87OzvTt25cVK1Zga2uLubk5165dyzfJQH688MILWFhYkJGRDkz4+92yuS4l\nIiJC8UwtXbqUefPmFbkOT09PfvzxR0JDQ7lw4QJbt25Fo9Hk8bb4+vqyYMECZfuHH37Az8+P1157\njYULFyrrDJ2cnAB48cUXSU9PZ+PGjcyePZsmTZpQt27dPOd3c3NjxYoVzJgxg5iYGCXr5YOICAEB\nI0hN3cmd4YOULgAAIABJREFUOy1JSXmeYcOGcfnyZSUzoI2NjeKVcnFxKXOfV06++eYb7OzsCp3V\nLw5vjEajoWfPnkB2n+j1+oces3PnTkaNGoVOp6Nr164kJSWRnJzM3r176devHwCdOnXKk5TlcTl6\n9Cjbtm0r0jFlKflNUT2CuZO0wKMkaSktr+OjkDPbbmRkJEZGRqxdu5bZs2dz5MgRoqOj2bNnjyKo\nDdlC2uHh4bz11lu4ubmxdu1aIiMjlUy6NWvWJCIigmHDhjFnzpzSurR8KYpHVUXlWUQ15FRKBI1G\ng7GxMY6Ojnl+7IyNjcnKylK2U1NTcx33IF27dmX79u3cunWLyMhIWrduTVZWFtWqVVMSUURFReX6\nYVIpPRo1asT48eNp0aIFTZo0wdfXl8qVK6PT6WjUqBH9+vXLtRD9wc/8we3+/fuj0QhgCM0rm5lM\nW7RowdGjR5WBUv369Ytcx9mzf3DixBlatWpDgwav8txz1bCwsMjTJwsWLCA8PBxHR0fs7e3Zv38/\nAJMnTyY9PR2tVsvmzZvZtGmTcoyI8OqrrzJ58mRiY2O5cOFCnvO3bNmSvXv3UqdOHQYOHMjq1auV\nY3MiIn+HGR4DbgAnMTfXYmlpqXyfS0qWQK/X06hRI/z9/bG1taVfv36EhITQokULbG1tCQsLKzB8\n+969e3Tu3BmdTodWq2XDhg1AdubD2bM/o149O7y9+1Cr1gtYW9vQrl27XOfu06c3ev0pdu78Gr3+\nVIGZeI2NjXMZNzmfcQ9LrX706FHmzp2LTqdjwIABZGRk8NxzzyEiPP/88xw4cICqVaty8eJFpkyZ\nQvPmzWnQoAH379/n3XffpXHjxgwaNEipz9zcnPfffx97e3vatWvHzZs3lWuOjIwE4ObNm9jY2JCR\nkcHUqVNZv349zs7ObNiwIVcCHRcXF7Zs2QLAqlWr6NatG23atKFt27aP/gGWMZ61cMmc2XZ1Oh27\ndu3i/PnzjxzaLv9ESyn06NEDePTJh6eJmoBG5d/OExlyGo3mvxqN5qRGozmq0Wg2ajQaixz7Jmk0\nmrN/7/d98qaqlBcMPwKtWrVSBoJ79uzh+eefx8zMDGtra2UAERkZme+AMidVq1bF1dWVMWPG0Llz\nZzQaDebm5tjY2BAcHKyUi4mJKaQWladFYGAQo0eP59o1My5cuMr774/D3d2d7777jlOnTrFjxw6C\ng4N5++23ATh//jxWVlbK8d99953itYDsz3Xw4MHPzECrIAwzyxkZu8jKSiUrK5SwsHDq16/Prl27\ncHZ2VspWr16ddevWER0dzfHjx/n666+VQfdXX33F77//Tvfu3Zk0aRIAAwYMUAyIhg0b4u3tjY2N\njVKf4Tt78eJFatasSUBAAIMHD1a+pxUrVsxjdGR7MWKBmsBxUlPPcfXq1Tx1lgR//PEHH374IadP\nn+bUqVMEBgayf/9+5s6dy+zZswucHDDIY0RFRRETE6NoFaanpzNjxv+RkrKJe/fuI/IL16/fYenS\npXnO/SjemFq1ahEfH8+tW7dIS0tT5DjyM4hzEhsbS2hoKP379ycqKor58+djampKrVq1OHr0KH37\n9lW+N7Vq1SImJoaDBw/y1ltvcffuXUaMGEFsbCwxMTHK8zA5ORl3d3eOHz9Oq1atmD59er5tNky+\nzZgxg969exMZGYmfnx+zZs2iTZs2HDp0iF27djFu3DhlvXNUVBSbNm1i9+7dBfZFeeBRDfTygOST\nbfftt99m7ty57N69m+joaDp16pRrcqEgz7uBR9F1Ky0ex6OqovIs8aQeud+AJiLiBJwFJgFoNJrG\nwBtAI6AjsERTXlbMqjwxho/6k08+yTddcK9evbh58yYODg4sWbJESRtcGL1792bNmjW8+eabyntr\n1qxh+fLlODk5YW9vryRaUCk9ijvMxcnJiQMHDvDJJ1OemYFWQfwzs7wI0AEDqVixlpJYpDAaN27M\nxx9/jJeXFzqdjg8++CCXMRMfH4+IFPg5GMru2bMHR0dHnJ2dWb9+vaI1N3ToULRarZLsxMjIiOXL\nl1C58jdUqLAcjcYZDw8XGjVqlKfOksDGxobGjRsD0KRJE9q0aQOAvb19vjPxBoPJII8xadIk9u/f\nr/RtamoqJiYvAncBL6A9Jib1uHXr1mO1z9jYmKlTp+Lm5kb79u1p1KgRGo3mod7nXbt24efnx/bt\n23F2diY2NpbU1FQyMzNxdHRk7ty5ijSBk5MTIoKDgwNnzpzB2NgYOzs7pU8M/WBkZMQbb7wBQL9+\n/RTv7aPy22+/8dlnn6HT6fD29ub+/ftcvHgRgHbt2j00qU95oSyHSxaFNm3aEBwcrHzXb926xcWL\nFzEzM3uk0HYLCwvu3LnztJr7xDxrHlUVlaLyRILgIrIzx+YhoNff/3cF1olIBhCn0WjOAu7A4Sc5\nn0r54Pz588r/P/74Y579lStX5n//+1+u9wwiu3/99RdWVlYMGDCAAQMGKPt79eqVZx1GQYKuKqWH\nwRh5FLHqhxEYGMSZM5eoWNGaBg0cHllUvrzyz8zyCgyCykZGPo88s9y/f3/F0MpJYGAQAQEjqFjR\nlnr17Fi+fEmeSQ/DwO3tt99WPD45+b//+z/+7//+L0/5wkSBc3rIP/jgg0e6hkclZ9hmTv03IyMj\nMjIyCgzfbtiwIZGRkWzbto3JkyfTtm1bJk+eTOXKlUlPPwtcAITimNUfNWoUo0aNyvXe1KlTlf+r\nV6+uPCu9vLzw8vJi8eLFWFlZER0dnev6AgMDFW/Iiy++qPTBtGnT6NmzJ3q9nhMnTiiebUM/5IfB\neMzZRzm9M/mxceNGGjZsmOu9Q4cOPdSTo/L0yZltNysri4oVK/Lll18qoe0vv/xyoaHtAwYMYNiw\nYVSpUoUDBw6Ui6yVxSVQrqJSHinONXKDAMMK6TrApRz7/vr7PRWVPDyJyK6acrhsUVxhLv/GBewl\nMbNc0v1YmBejJL+bDwvbtLa2JiIiAsgdvn3lyhVMTU3p27cvH374oRI6amJiwtSpk6hceSoazXoq\nVWrF8uVLMDZ+ornOItO6dWs2bNigCHsnJCTg4eFBYGAgAKtXr6Zly5ZAtvF17tw5pX8L6pOsrCwl\nBH3NmjXKIN7a2prw8HAAZa0gZK+py+mRad++PQsXLlS2jx49WizXqlJy+Pn5ERUVRXR0NGFhYUUK\nbe/ZsyenTp1Skp3k3O/i4sKuXbtK5ZoexrPiUVVRKSoPNeQ0Gs0OjUYTk+N17O+/XXKU+RhIF5HA\nEm2tSomSXyKAyMhIvL29cXNzo2PHjly7do3Tp0/TtGlT5Ti9Xo9Wm+2BiYiIyFMeKFA/Kb+BZv/+\nAxStpMJ4EgNQpWQoLmPk37qAvbjX6pRWP5b0dzOnlyC/cMVevXqRkJCQJ3z72LFjuLu7o9PpmDFj\nBlOmTFGO6dDBl4sXzzB//lxefbUu//3vZ7lCuZ8GD4bIjhs3jkWLFrFixQqcnJxYs2YNCxYsIDAw\niA0bNjF9+lfUq2fHTz9tKbBPqlatypEjR3BwcGDPnj2KV3DcuHEsXboUFxcXxXCE7Gd1bGyskuxk\nypQpSgIde3v7XF5FlWcfdbJURaWMU1S9ggdfwEAgFKiU472JwIQc29uBpgUcL5988ony2r17dzEo\nMag8Dhs3bpShQ4cq24mJieLh4SE3btwQkWwNt0GDBomIiE6nk7i4OBER+c9//iOzZs2S9PT0AssX\npJ/0OBo+IkXTdFJ5+jypzpL6+RYPpdGP6mdXshS1f83MzJ5yC599iqtP4+LixN7evljqKglUfTYV\nlZJl9+7duWwgHkNH7kmNuA7ACaD6A+83BqKAioANcA7QFFBHyfaSyiNz5swZsbGxkYkTJ8q+ffvk\n+PHjYmFhITqdTpycnESr1UqHDh1EJFtg+D//+Y+IiDg7O8u5c+cKLe/t7S0HDhwQEZFr165Jw4YN\nRST7h8zIyETAVsBB4L9iZGQsO3fuFL1eLw0bNpSbN29KVlaWtGzZUnbs2CEiRTMAMzMzS7zvVIof\nwyDCwkKnDiKegKfdj487OVPalHWRZwNF7d8HxdyflPLSTyVJcfXpg8LxZQl1QkZF5enzOIbcky4A\nWPS3sbbj71COQyIyQkRiNRrNerJzU6cDI/5uoEoZJmcigClTpuDj44O9vb0SBpmT3r174+fnR48e\nPTAyMuKVV17h+PHjBZaH/FMYZ4dituLgwShMTOpx//7/Ub/+q1SrVo26desyceJEhg0bhru7OxqN\nhg8//BCNRsPNmze5e/cy8DWwCkgkOfkcNWvWBLKz2vXu3ZudO3cyfvx4bG1tGTZsGCkpKbzyyit8\n9913z0y2tWcVdQF78fC0+zH3OsnspC1lPR34PwlhsttelhPrFLV/izMDYXnqp6dBcnIy3bp14/bt\n26Snp/Ppp5/StWtX9Ho9HTt2pEWLFhw4cICXXnqJn376iUqVKhEREUFAQAAajSaPTmFZojgTV6mo\nqJQgRbX8ivuF6pErM1y+fFlSU1NFRGTr1q3SqVMnadiwoRw8eFBERNLT0+XEiRNKeTc3N+nfv7/M\nmTNHRETu379fYHlvb2+JiIgQEZEbN26ItbW1iPzjBXz33Xflm2++kevXr+cqKyLSvn17adCggSQl\nJSn1tmrVSkaMGCVGRsZibu4opqZW0qdPX/n0009FRMTa2lppl4iIVquVffv2iYjI1KlT5b333ivm\n3lNRUTFQnryp5dHzUBr9Wx77qaQweOQyMjLk7t27IpL9u9agQQMRyfa0mZiYSExMjIiIvPHGG7Jm\nzRoRyf4t2r9/v4iIfPjhh6pHTkVFRYFS8MipPEMcO3aMDz/8ECMjIypWrMjSpUsxNjZm9OjRJCYm\nkpmZyXvvvafoN/Xu3Zvx48czc+ZMIDvzW3BwcL7lC9JPyukFXLZsGVevXs1VNiUlhT///BOApKQk\nqlatyrvvvkvr1q1xdXUlKCiQ55/P1oCKiYnGzOyfdNi9e2fPFN+5c4fExEQlW9uAAQMUXSUVFZXi\npzx5U8uj56E0+rc89lNJIyJMmjSJvXv3YmRkxOXLl7l+/TqQHRXi4OAAZGd7jIuLIzExkcTERDw9\nPYFsyZDt27eXWvsLw5C4KiDABxOTeqSn61V9NhWVMohqyKko+Pr64uvrm+f933//Pd/yH3zwQR59\nKK1Wm2/5oKAg4uLiiI+Pp0aNGop+0pUrV7CysqJv375YWlry7bff5jpuwoQJ9OvXj3r16jF48GB6\n9erFpUuXWLJkCVu3bqV9+/asWbMm3/apGkcqKqVHjRo1ysWgrzyGgsLT79/y2k8lyZo1a7hx4wZR\nUVEYGRlhY2OjaPLl1DqsUKGC8r6Uo1Um5WlCRkXl30px6sipqORLYanIC0oHDrB3717Cw8OZMGEC\nffr04d69e0yePJnVq1cD0LRpU0JDQ/njjz+AbPmEs2fP5jm/hYUF1apVU9bu/fDDD3h5eZXkJauo\nqJQTSkK/71lE7ad/MBhjiYmJ1KxZEyMjI3bv3o1er89TJieWlpZUq1ZNkdcpaBKyLKHqs6molG00\npT07pNFopLTboPLodO7cmbVr12JhYVFgGR8fH+bNm4ezszPx8fHUq2dHSspuQIBQTE2noNefKvIP\nw6BBg/jtt9+UhCaurq707duX8ePHk5aWhkajYebMmXTu3Jn69esTHh6uCJnGxMTwzjvvkJKSQv36\n9VmxYoWa7ERFRUUhPj5e9Tw8Avn1k16vp3Pnzhw7dqyUW/d0sLCw4M6dO9y8eZMuXbqQnJyMq6sr\nhw4d4tdff0VE6NKlCzExMQDMmzeP5ORkpk6dSmRkJIMGDcLIyAhfX1+2bdumlFNRUfl3o9FoEBHN\nw0vmOKa0jSjVkHv2yGnIhYWF0a7dMBITI8jOLhmOhUUoO3d+jZubW6H1qAMrFRUVlbKPXq/PZbgU\nBz/99BO2trbY2dkVW53m5ubcvXu32OpTUVFRKU4ex5BTQytVCmTNmjU0bdoUZ2dnhg8fTlZWFjY2\nNiQkJADw6aefYmdnR6tWrejbty+ff/65cuz69etp2rQpffv2JSXlLBABTAXWcvduzEN/8AsLx3wc\n4uPjCQsLIz4+/onqUVFRUVHJS0ZGBkOHDsXe3p4OHTqQmpqKj48PkZGRANy8eRMbGxsAVq1aRY8e\nPfD19aV+/fp8+eWXfPHFFzg7O+Ph4cHt27fZvHkzCxYsUELv/fz8lHVm/v7+jBkzBk9PTxo0aMCm\nTZsAyMzMLLSNDybdKowFCxYo5ytO1N8iFRWV4kQ15FTy5dSpUwQFBXHgwAEiIyMxMjJizZo1yg9h\neHg4P/74I8eOHWPbtm2Eh4fnOj4zM5PDhw+zcOFCXn3VGlNTXypXhgoVklmzZg0BAQEFnjs+Pp6A\ngBGkpOwmMTGClJTdBASMeOwfvuI2ClVUVP7hp59+4tSpU6XdDJVS5uzZs4wePZrjx4/z3HPPsXHj\nxlyG06VLl/jrr78YOnQoH3/8Mb/99htBQUGMHj2aMWPG8MUXX/DKK6/g6urK9OnT+fnnn9m+fTsZ\nGRls3LiRsLAwpk2bBkBqairLly8nNDSUgIAA3n77bdq0aUPbtm1JTk6mbdu2uLq64ujoyM8///xY\n1zN//nzu3btXpGOysrIK3a/+FqmoqBQ3qiGnki8hISFERkbi5uaGTqdj165dXLhwQdkfGhpKt27d\nMDExwczMjC5duuQ6vmfPnkB22uW0tFT0+lOMHz8Qf//+DxWQNaS5zs6MBjnTXBeV4jYKVVRUcrN5\n82ZOnDhRpGMe5jlRKX/Ur19fSbfv7Oyc7/M6PT2d0aNHM2vWLGrXrs2vv/7KwIEDeemllzh8+DB2\ndnYkJCSg0Wjo2rUrgwYNwszMjO7du3Pt2jUlsRVAlSpVAHjxxRe5d+8emzZtYvfu3ZiamrJ582bC\nw8PZtWsXAQEBLF68GIC0tDTatGkDwO7du+nXrx8jRozAzc0NBwcHpk+fDsCiRYu4fPkyPj4+Svnf\nfvsNDw8PXF1d6d27t2Lk2djYMHHiRFxdXQkODi6wf9TfotIlp3c4J6tWrWL06NGl0CIVleJBNeRU\n8kVEGDBgAJGRkURFRXHy5EmmTp36yMcbUi9XqFCBjIwMatSoQXR0NDdv3nzosbnTXMOTpLkuTqNQ\nReVZYe7cucrgduzYsXkGtzt27Mh30Dpx4kSaNGmCk5MT48eP5+DBg/z888+MHz8eZ2dnLly4wPnz\n5+nYsSNubm54eXlx5swZIDscbvjw4TRv3pwJEyYwffp0AgIC8PHxoUGDBixatKh0OiMfFi5cSOPG\njenfv39pN6Xc8GC6/YyMDIyNjRUvVVpaGiYmJoqxV7t2beLi4jh27BhXr16lbdu2rF27litXrpCR\nkQHA4sWLWbJkCTExMVhbW5OWlqacI6e3z9jYWElelZWVxaRJk3B0dKRt27YkJSWxY8cOIHsCITk5\nmczMTPbt24eXlxezZ88mLCyM6Oho9uzZw/Hjxxk9ejR16tRhz549hISEcPPmTWbNmkVISAjh4eG4\nuLjkWkrw/PPPEx4eXqg+qfpbVHo8zFNalJBbFZWyhmrIqeRLmzZtCA4OVmYLb926xcWLF5WUyp6e\nnmzZsoW0tDSSkpLYunVrgXUZjnn77bcLzXZpoKhprrds2cJ///tfAKZPn678wPr7+3P8+PG/jcJe\nwClU7SMVFWjZsiX79u0DICIiItfgVqvVMnPmzDyD1oSEBMX7dvToUSZPnkzz5s3p2rUrc+bMITIy\nEhsbG4YOHcrixYsJCwtjzpw5DB8+XDnvX3/9xcGDB5k7dy4Ap0+fZseOHRw+fJjp06eXGU/d0qVL\n2blzJz/88MNDyxZ3m8tKHxSV/JKWWVtbK2H3v/zyS659Go2G9PR0Bg4cSPXq1dm3bx9Tp04lPT1d\nKZOamkrt2rVJT09XhLah8D7Kqe0WFRVF7dq1OXr0qJLkpHnz5oSFhbFv3z5atmzJunXrcHFxQafT\nERsbS2xsrHI9hms6dOgQsbGxeHp6otPp+P7777l48aJyzt69C48yMfRFcU1Q/pt42KTTunXr0Gq1\naLVaJk6cqBxnbm7OuHHj0Ol0HDx4MFedK1aswNbWlmbNmimyRCoq5RVVEFwlXxo1asTMmTPx9fUl\nKyuLihUrsnjxYmXmytXVla5du+Lo6EitWrXQarXKjGhaWhq9evWiXbt27N27l2vXrpGamkpwcDBh\nYWE4Ozvz559/MmLECLZs2UJGRgYbNmzg1Vdf5d69e4wePZoTJ07QsOFLDBjQj/79+xeatbJLly55\nQjsNWFpasnz5EgICRmBi0pf0dP2/VvtIRcWAi4sLERER3L17l0qVKuHi4qIMbrt27aoMWkWE9PR0\nPDw8sLS0xNTUlMGDB/Paa6/RuXPnPPUmJydz4MAB/Pz8lEFwzoG5n59frvKvvfYaxsbGVK9enVq1\nanHt2jVefPHFkr34hzB8+HDFqzhgwAD27dvH+fPnqVq1Kt988w329vZMnz6dP/74g/Pnz1OvXj18\nfX3ZvHkzycnJnDt3jg8++ID79+/zww8/ULlyZbZt28Zzzz3H+fPnGTlyJDdu3KBKlSosW7aMV199\nFX9/fypXrkxUVBQtWrRQDN3yxINeDY1Gw7hx4/Dz82PZsmV4enrme1xSUhKmpqakp6fn0lUzNzen\ne/fuuLu7U7NmTV544QVu3LgBkEuv7UHy03bz8PBg5cqVVKhQgZYtW7J7927++OMPKleuzLx584iI\niMDCwgJ/f/98E5yICL6+vgXqvlWtWvWh/WOYoAwI8MHEpJ76W/SItGzZks8//5xRo0YRERHB/fv3\nlUmnV199lYkTJxIZGclzzz1Hu3bt+Pnnn+natSvJyck0b948z3fp6tWrTJs2jaioKCwsLPD29sbZ\n2ZlPP/2UNWvWkJqaSkJCAs8//zw1atTgypUrQPZ9WrduXRo2bEjlypUxNzcnODiYtLQ0XnrpJS5d\nukS1atWoVKkSxsbGNGvWjD59+vDJJ59w48YNEhISlONXrFhBlSpVmDhxIlu3bsXY2BhfX19lQlpF\npSioHjmVAvHz8yMqKoro6GjCwsJo2rQp58+fV7TZPvjgA06dOsX27duJi4vDxcUFgMDAQP766y9G\njx7NqVOnaN++PXPmzEFE+M9//kNkZCRVq1alZs2aREREMGzYMOVhO2vWLNq0acOhQ4fYu3cvixcv\npmXLlvj7+2Nra0u/fv0ICQmhRYsW2NraEhYW9tAY9z59euPs3IjFi8ei158CsgqcwZs8eTJOTk54\neHioaxdUnlmMjY2xtrZm5cqVeHp65hrc1q9fH19fXyWs+vjx43zzzTdUqFCBI0eO8Prrr7N161Y6\ndOiQp96srCyqVaumHGs43sCDA96c4XhGRkZKSF1psnTpUurUqcPu3buJi4vD2dmZ6OhoZs2alSvU\n8uTJk+zatUsZ3J84cYLNmzdz5MgRPv74Y8zMzIiMjKRZs2Z8//33AA/1Vh46dKhcGnH16tXLlYn4\ngw8+YOrUqbz66qtER0cTERHBBx98oEgJDBgwgB49eqDRaPj000+pUKEC3bp1o1GjRjRo0ICFCxfy\n5ptvEh4eTrVq1QgMDGTjxo0kJyfj4uJC+/btc91Lw4YNU/5/6623CAsLw9HRkdWrV9OoUSPc3NyY\nO3cuxsbGtGjRgq+++gqdTsedO3cwMzPD3Nyca9eu8euvvyr1GLTiAMVzY1ijd+/ePc6ePVvkfurT\npzd6/Sl27vwavf7UQ9eLq+SddMrpUa1WrRre3t5YWVlhZGTEW2+9xd69e4Hs8F7DWv2cHD58GB8f\nH6ysrDA2NqZ3795cu3aNH3/8kZUrV/Lcc89Rq1YthgwZws2bN3nvvfeIjo5m5MiRDBkyBDs7O86e\nPcvt27d57bXXcHJy4s8//2T27Nncvn0bExMT1q1bR2RkJBMnTmT9+vW88MILjB07lh49euSJcDh+\n/LgS4aCi8jioHjmVx2bo0KHExsaSlpbGwIEDcXJyUvbZ2Njg4OBAYGAQW7f+j19/PUxGxk1q135R\nebj26NEDyH5Q//jjj0D2gvItW7YwZ84cIHs2/88//2TTpk00btwYV1dXAgMD2b9/Pz///DOzZ89W\nBgSFYWJiQpMmTcjIyGDixIlERUXlO4Pn4eHBzJkzmTBhAsuWLeOjjz4qia5TUSl1WrZsydy5c1mx\nYgX29vaMHTsWV1dXmjZtysiRI/njjz945ZVXuHfvHn/99ZeSVKJDhw40b96cBg0aANkTIIYBr7m5\nOTY2NgQHB/P6668DEBMTg1arLbAdZRURYf/+/Upqex8fHxISEkhKSgKga9euVKxYUSnv4+NDlSpV\nqFKlCs8995zisXRwcODYsWNF9lY+a+Rn7Bl455138pT38PDIk0QnOjoayE4c0qVLF+Lj4xkwYAAD\nBgxQylSvXp0DBw4o5eLi4rh06RJLlizh9u3bmJqaYmpqSqtWrdBqtTg5OdGoUSNefvllWrRoodQz\nZMgQOnToQJ06dQgJCWHFihX06dOHtLQ0NBoNM2fOpGHDhkVeX1WjRg3VC1cEHpx00mq1yqRTztDd\nBzE1NS3ws3kwDPjKlSt069aNw4cP06NHD+7cuaNEKixevJgvv/ySy5cvK99tExMTunTpwm+//UbP\nnj25evUqr732GosWLcLV1ZW4uDiqV69OWFgYXl5e6PV6jhw5QtWqValdu/YjRzioqDwKqiGn8tgU\nFGYC2TPthixdGRnDyciwAKJYsuQbPvpoolIG/lkYD9kP2I0bN9KwYUMgO4TG19eXxo0bA9CkSRMl\nRt7BwaHIC8XDwsKU2ThAmcEzDMo6deoEZBuXO3fuLFLdKirliZYtWzJ79myaN2+ea3D7/PPPs3Ll\nyjyDVnNzc7p166aEnn3xxRcAvPnmmwwZMoRFixYRHBzMmjVrGDZsGDNnziQjI4M333wTrVb70AFv\nWUuJo5IIAAAgAElEQVQ48LD2FOZd1Gg0yrbB05jTW/ko9ankT2BgEAEBI6hYMXvN2fLlS/L1bD1Y\nbuXK7zE1NQXIJZexYsWKfM8zatQoRo0apWz7+Phw5MgRZdugB3f48GHl90SlZCho0snNzY13332X\nhIQELC0tCQwMZMyYMUD+azYBmjZtynvvvcetW7cwMzNjw4YN+ZYTEbZv387QoUPZuHEjkydP5o8/\n/sDLy4uZM2cq3+/KlStTqVIljIyMlL8ZGRloNBolKVRgYGC+46UjR44QEhLChg0bWLx4MSEhIcXU\nYyr/JlRDTqVEEBElS1dKyotAElCNChVqFGp8tW/fnoULFyoZ7E6cOJEn/OrBAdLjtC0/TExMlP9z\nGpcqKs8irVu3zpUFMOfg1tvbO9eg1cDhw4fzvJef5yRniJqB7777Ltf2iBEjiIuLIz4+nho1auTy\n1pQ2hmdEq1atWL16NZMnT2bPnj08//zzmJmZPVadz5K3srTImcI/JUULxBAQ4EPbtq1zebketdzj\n8qjGpErxUNCkU+3atfnss8/w9vYGyOXZym/NJmRnS502bRrNmjWjWrVqODk5cevWLbZs2cLChQsZ\nNWoUd+/exd/fn+TkZMzNzZXMp9bW1vkaZIbnRc6xRY0aNQgJCaFWrVqEhoZy/PhxKlWqRJ06dQqN\ncFBRKSqqIadSImg0mhxZuq4AFsAtMjPjsba2LnC2e8qUKbz33ntotVpEhFq1ahVoeD0O7u7ujBkz\nJt8ZPBUVladDWR8IG55Pn3zyCYMGDcLR0ZGqVasqa90e9fgHWb16NcOHDy+yt1Ilm38mB/Om8M9p\noD1qucehpI1ElbwUNunUu3fvfLOGGsK9DezatUv5v1OnTjRu3Bhra2vlM5sxYwaDBg1Skp2sWrUK\nR0dHFi1ahJmZGVFRUcTGxtKnTx9lMtrwvc351/C/qakpo0aNYuTIkVSoUAF3d3dq1aqFpaVloREO\nKipFxpBit7Re2U1QeVZZu3admJpaiYWFTkxNrWTt2nVFOj4uLk4cHByUbX9/f9m4cWOufatWrZLR\no0eLiMi0adNk3rx5ecr6+PhIRESEiIisW7dOHBwcxMHBQSZOnKjUbW5urvwfHBws/v7+j3HFKioq\nhXH9+nUxNbUSiBYQgWgxNbWS69evl3bTHgkzM7NirS/nM6ssEx4eLmPGjCm0zJ49e6Rz584l1oZH\nvXdK8h47cuSIWFo6/11v9svCQidHjhx54rpVSh7DmMTS0jnXmCQpKUlEROLj48XV1VUOHjworq6u\nEhUVVZrNVfmX8bdNVCQ7SiPF6O14HDQajZR2G1RKFsOC85yzX8VRVkVFpfwRFhZGu3bDSEyMUN6z\nsHBm586vcXNzK8WWPRo5sxk+KfHx8Xz88ce89NJLTJ06tVjqLE1+//135s2bx88//1xi5zB4c3Om\n8C9sjdzDyhWFzp07s3DhQuzt3UhJ2U22uHcMpqY+6PWn1N+sMk58fDz16tnl+9m99957xMbGcv78\neczMzLC0tGTgwIGMHz/+ic5X0HhGHeuo5IdGo0FEihaiUVTLr7hfqB45lb8paKbsaXH9+nU5cuRI\nufEMqKiUR8q7Ry6n537cuHFib28vWq1WgoKClPc/++wzcXBwECcnJ5k0aZKIiCxbtkzc3NzEyclJ\nXn/9dVm58nsxNbWSSpVeEGNj0xJ/3iUnJ8trr70mTk5O4uDgIOvXr5eQkBDR6XSi1WolICBA7t+/\nLyLZXicPDw9xdHSUpk2bSlJSUi5v25EjR6R58+bi7Owsnp6ecubMGRHJ9sh16dKlRK9D5NGf1SX1\nTH8w0mTNmsBirV9E5LXXXpPExMRir/ffzNP0phY2nintsY5K2YXH8MiphpxKmaC0B3fqg1VF5enx\npCHXpYnBkAsODhZfX18REbl27ZrUrVtXrl69Kr/++qt4enpKamqqiIjcunVLREQSEhKUOt5//30x\nMany9/NumsAHJf6827hxowwdOlTZTkxMlJdfflnOnTsnIiJvv/22LFiwQO7fvy/169dXQtHv3r0r\nmZmZuYw0w3siIjt37pRevXqJyNMz5J4G3bt3F1dXV7G3t5dly5aJiIi1tbXcvHlT4uLipEGDBtKp\nUydp1KiRXLx4sdjPn5WVVabqeRZ4WuOMws5T2mMdlbLN4xhyqiC4SpnAsDg9O9wBci5OL2lyLl5P\nTIwgJWU3AQEjVEFwFZUS4lkQRg4NDaVPnz4A1KxZU8n0uXPnTvz9/ZXsus899xwAx44dU7TLgoKC\n0Giq8M/z7sUSf945ODiwY8cOJk2axP79+4mLi6N+/fq88sorQLZI9969ezl9+jQvvvgizs7OAJiZ\nmWFklHuocPv2bV5//XUcHBwYO3YssbGxJdbu0mLFihWEhYURFhbGggULSEhIyJWU5sKFC0ybNo3Y\n2FhefvllIFsovHPnzuh0OrRaLRs2bCAyMhJvb2/c3Nzo2LEj165dA+Dbb7/F3d0dnU6Hn58fp0+f\nxs7OjgEDBmBvb0+FChVISEhg0qRJLFmyRDnv9OnT+fzzzwGYO3cu7u7uODk5MX36dCBbssdQj4OD\nA3/++efT6rIyT40aNVi+fAmmpj5YWDhjaurD8uVLij20sbDxTGmOdVSeTVRDTqVM8E+GS0MK8hjS\n0/VYW1uX+LnVB6uKytOnRo0auLm5PTPrQ0Sk0OyTAwcOZMmSJcTExDBhwgQyM+/wz/Pucok/7xo2\nbEhkZCQODg5MmTKFzZs3F1hWHrJufcqUKbRu3Zpjx46xZcsWJfPes8T8+fNxcnKiWbNm/Pnnn5w9\nezbX/nr16uVZ07l9+3bq1KlDVFQUMTExtG/fntGjR7Nx40bCwsLw9/fno48+AqBXr14cOXKEqKgo\n7OzsWL9+PefOnWPUqFEcP35cuRd69+7N+vXrlXOsX7+e3r17s2PHDs6ePavUER4ezv79+wGUeo4d\nO6YYmSrZPI1JpMLGM6U51lF5NlENOZUywdOaKcsP9cGqoqLyqBiMnJYtWxIUFERWVhbx8fHs27cP\nd3d32rVrx4oVK0hJSQHg1q1bACQlJVG7dm3S09PZsmULLVp4YGrqQ6VKX2FsvKTEn3dXrlzB1NSU\nvn37Mm7cOA4ePEhcXBznz58H4IcffsDb2xtbW1uuXr1KRESE0u7MzMxcdSUmJlKnTh2gYEHt8szv\nv//Orl27OHz4MEePHsXJySmPsZqfgPuDXs9Lly5x/Phx2rVrh06nY9asWVy+fBnI1hA0eGjXrl3L\nmTNnchmHhvvMycmJ+Ph4rl69SkxMDFZWVtSpU4fffvuNHTt24OzsjLOzM6dPn1aMzfyMTJV/KOlJ\npMLGM6U51lF5NlF15FTKDH369KZt29ZPPZOT4cEaEOCTK8OZ+mBVUVF5EIPXrUePHhw6dAhHR0eM\njIyYM2cONWvWpH379kRHR+Pq6kqlSpXo1KkTM2fOZMaMGbi7u1OzZk2aNm3K3bt32bBhvZK1siQ8\nAz4+PsybNw9nZ2e6d+/OvXv30Gg03L17l40bN5KYmEi3bt24dOkSr7/+Ou+88w4mJiYEBQUxatQo\nUlJSqFKlCjt37sxV7/jx4xkwYABDhgzBx8en2Ntd2iQmJlKtWjUqVarEqVOnOHToEJDbU5mf19Lg\n9dy2bRtTpkzBx8cHe3t7QkND85T19/fn559/xt7enlWrVvHLL7/kaxwC+Pn5sWHDBq5evapopokI\nkyZNYsiQIbnK6vX6AuspK+j1ejp37syxY8ceqfyqVato3749tWvXBmDBggW88847VK5cGQAbGxsi\nIiKwsrIqsTYXlcLGM6U11lF5RinqorrifqEmO1EpI6hZK1VUVJ4lvL29laQlBi5cuCD29vbFUv/A\ngQMVrc5nibS0NOnYsaM0btxYevToIa1bt5Y9e/aIjY2Nkuwkp76pgcuXLytJbrZu3SqdOnWShg0b\nysGDB0VEJD09XU6cOCEiIjVq1JD4+Hi5f/++tGvXTvz8/HJ9LobEKiIiJ06cEA8PD7G1tZWrV6+K\niMhvv/0mzZo1U/TP/vrrL7l+/bqSoKUgctb7pDyupmJB/VcQ3t7eEh4ermxbW1vLjRs3lG3D56Ki\nUt7hMZKdqB45FZW/MYQ9qKioqJQUT6Ifpdfr6dChAy4uLkRGRmJvb8/3339PaGgoH374IZmZmbi5\nubF06VJMTExyHWvwWkyaNInz58/j7OxMu3btGDFihOIdycrKYsKECWzfvp0KFSowZMgQRo4cyaef\nfsrWrVtJSUnBw8ODr776Sqk3MTGRsLCwZ8qzULFiRbZt25bnfUMYqpWVFTExMXn2Hzt2jA8//BAj\nIyMqVqzI0qVLMTY2ZvTo0SQmJpKZmcl7771H48aN83hor1y5kmuNZc7/GzduzN27d3nppZeoVasW\nAO3atePUqVM0b94cAHNzc1avXk1wcDBdunQp8NoKW8dZVJ6krvT0dPr166fcx6tWrWLu3Ll57rON\nGzcSHh5Ov379MDU1ZeDAgVy+fJnWrVvz/PPPExISkss7umbNGhYuXEh6ejpNmzZlyZIlxXrNKipl\njqJafsX9QvXIqaioqKj8C3hSmZO4uDjRaDSKhycgIEBmzpyZr4yASG6PXEHepJzbS5YsET8/PyVl\nvUE6wfBXRKR///6ydetWERFp1cpLKlY0+1fJtpT1yA2Dl+zKlSvSqlUr0el04uDgIPv37xeR3B65\n/CQWDHV8/PHH4ujoKM2bN1eu9cKFC9K8eXPRarUyefJkRYqjoHMVxIP38aBBg2TevHkF3mfe3t4S\nGRmp7LOxsckl52G4ppMnT0qXLl0kIyNDRERGjBghP/zww2P0oopK6YAqP6CioqKiolL2KC6Zk7p1\n69KsWTMA3nrrLUJCQvKVEXgQeUgmSoCQkBDeeecdxYNhkE4ICQmhWbNmaLVadu/ezYkTJ4iPjyc0\n9CD370//18i2BAYGUa+eHe3aDaNePTsCA4Meu674+HjCwsKKrb8M9RlYu3YtHTp0IDIykujoaJyc\nnPIc86DEgiExT3JyMh4eHhw9epSWLVuybNkyAMaMGcPIkSOJjo7mhRdeKNK5HiTnfdyvXz/27dvH\nrl278txnBnLev/KPIwD4xzMYEhJCZGQkbm5u6HQ6du3apXhRVVSeVVRDTkVFRUVFpYQpKZkTg7FV\nUqSlpTFy5Eg2bdpETEwMgwcPJjU1lbi4OIyMzADrv0s+27Itxak3WpwG4YP1JSUlERgYhJubGytW\nrGDGjBnExMTkmwClIIkFQ5IeABcXF+UzDQ0N5c033wSgf//+Sj2Pcq4HeTDcUaPR5HufFQURYcCA\nAURGRhIVFcXJkyeZOnVqkepQUSlvqIacioqKiopKCVNcMicXL17k8OHDQLYnxM3NLV8ZgYIwNzfn\n7t27+e5r164dX3/9tSI3cOvWLVJTU9FoNFSvXp2kpCSCg4OV68nKSgLinuh6ygvFZYgXp0GYX31Q\nlYCAEdjZ2bF3717q1KnDwIEDWb16da7jCpNYyLm+skKFCmRkZADZxpbBAMvpEWvZsmWh58oPvV6f\n6z5u2bIlQJ77DLLv2Tt37ijbFhYWubYNbWnTpg3BwcFKX966dYuLFy8+tC0qKuUZ1ZBTUVFRUVEp\nYYpLP8rW1pYvv/ySxo0bc/v2bcaOHcuKFSt4/fXXcXR0pEKFCrzzzjsA+SbPsLKywtPTE61Wy4QJ\nE3LVPXjwYF5++WW0Wi06nY7AwEAsLS0ZPHgwTZo0oWPHjri7uyvX06KFBxUrfvKv0MMqLkO8uD2z\neevTYGJSj0OHDlGzZk0CAgIYPHgwkZGRuY4rSGIBCg7D9fT0JDAwEMhOKmLg4sWLhZ4rP+zs7JT7\nODExkeHDh+d7nwEMHDiQYcOG4ezsTFpaGkOGDKFDhw60adMm+4r/vrcbNWrEzJkz8fX1xdHREV9f\nX65evfrQtqiolGc0jxI3X6IN0GiktNugoqKiMn36dMzNzXn//ffLZH3FQXR0NJcvX6Zjx46l3ZR/\nLU+atbIo+lvFwZYtWzh58iTjx4/Pd7/hek6fPs3ly5cZP358rnv/k08+wcvLi9atW+fR/ypvBAYG\nERAwIpfeaFH1/+Lj46lXz46UlN1kG18xmJr6oNefyvd+aNGiBfv37y+wvnr16nH9+h1SU3//uz4z\nTE0r8d//Tufrr7/GxMQEc3NzfvjhB+rWrUv9+vUJDw/HzMyM7t27o9frsbW15fbt2xw6dIh79+7l\n8nht3LiRX375he+++464uDj69u1LcnIy3bp1Y/78+dy5c4fvv/+eOXPmKOf6/vvvqVevXpH6RUVF\nJXtSQkSKlGZVNeRUVFRU+HcYcqtWrSI8PJxFixaVdlNUHgO9Xk+XLl3yTX3/NHhUI7Sge/9xhJuz\nsrIwMio7wUNPYogbKA6D0ED9+vWZNOljxowZ/8T1PRiyWN4ojs9GRaU0eRxDruw8HVVUVFSeMrNm\nzcLW1pZWrVpx+vRpIFsrqmPHjri5ueHl5cWZM2e4c+dOrhCqe/fuUbduXTIzM/Mt/yBHjx6lefPm\nODk50atXLxITEwHw8fHhvffeQ6fTodVqCQ8PB7IHwgMHDqRVq1bY2Njw448/MmHCBLRaLZ06dVLW\nMEVGRuLt7Y2bmxsdO3bk2rVrSr0TJ06kadOm2NnZERoaSnp6OlOnTmX9+vU4OzuzYcOGkuxalSKy\nZs0amjZtirOzM8OHD0dE2L59Oy4uLuh0Otq1a0e9evX4/fff6dGjB46Ojnh4eHD8+HEg+54JCAjA\nx8eHBg0a5DLWP//8cxwcHNBqtSxYsADINgobNWqEv78/tra29OvXj5CQEFq0aIGtra1yL65atYqB\nAwdSp04dateug7t7M2rWrEXt2rVxdHTE1taWsLAwVq1axejRo/Ncl7+/P5s2bWLRokVcvnwZHx8f\nJSRuxIgRuLu74+DgwPTp05VjbGxsmDhxIq6urnz22We4uLgo+86dO5dr+2lTo0YN3NzcnshQ6NOn\nN3r9KXbu/Bq9/lShRpe5uTkAV69excvLC2dnZ7RaLaGhoUB2GGSvXj3Q60+h01lga1uX2bNn8u23\n3+aqY/LkyTg5OeHh4aGsIYuLi8PDwwNHR0emTJnyWNdS3Nk3H5fiTiCjolJuKKpeQXG/UHXkVFRU\nSoGIiAjRarWSmpoqd+7ckQYNGsi8efOkTZs2iibX4cOHpXXr1iKSrbm0Z88eEREJCgqSIUOGiIgU\nWH7atGkyb948ERHRarWyb98+ERGZOnWqjB07VkSy9ZGGDh0qIiJ79+4Ve3t75diWLVtKZmamREdH\nS5UqVeR///ufiIj06NFDfvrpJ0lPTxcPDw+5ceOG0qZBgwYp9Y4bN05ERLZt2yZt27YVEZGVK1fK\n6NGji78znwE2b94sJ0+eVLanTp0qISEhxXqOPXv2SOfOnfO8n5/+1apVq+Tll18WvV4vIv9ouY0e\nPVpmzJghIiK7du0SJycnEcm+Zzw9PSU9PV1u3Lgh1atXl4yMDAkPDxetVispKSmSlJQkTZo0kaNH\nj0pcXJyYmJjIiRMnRETExcVFAgICRETkp59+ku7du4tI9j3Tu3dvAQQ8BRYINBYjo4py/vx5peyq\nVauUeyvnvT9w4EDZuHGjiGTrfeXU/zJcU2Zmpnh7e8uxY8eUcnPmzFHKtW7dWqKjo0VE5KOPPpLF\nixc/wadQvjBotc2bN09mz54tIiJZWVmSlJQkIrl14Qz9mZKSIvb29kpfazQa+eWXX0REZPz48TJr\n1iwREenatausXr1aRES+/PJL5VyPypPqIhYX169fF1NTK4FoARGIFlNTqzKr9aeiUhA8ho6ccema\nkSoqKir5k5iYyNq1axk+fHiJ1L9v3z569OhBpUqVqFSpEt26dSMlJYUDBw7g5+enLPhPT08H4I03\n3iAoKAgvLy/WrVvHyJEjSU5OLrC8gTt37pCYmEiLFi2AbJ2vN954Q9nfp08fIDvz2927d5XQpo4d\nO2JkZISDgwNZWVn4+voC4ODgoKxJOn78OO3atUNEyMrK4sUXX1Tq7dmzJ5CdPlyv1xd7/z1rbN68\nmc6dO2NnZweQy0NUnDyYdh1y61+JCKmpqRw5cgQvLy/q1q0L/CMzsH//fjZt2gRke14TEhJISkoC\n4LXXXsPY2Jjq1atTq1Ytrl27RmhoKD169FDWpfXs2ZN9+/bRpUsXbGxsaNy4MQBNmjRRPGUODg65\n7pmkpCSMjCqRlXUGGA5EUKlSMjdu3FDux0fF8D0BWLduHcuWLSMjI4OrV68SGxuLvb09AL17/+Ol\nCggIYMWKFcybN4+goKBcemn/Ftzc3AgICCA9PZ1u3brh6OiYp8z8+fPZvHkzgCIn4O7unkdOYOfO\nnUC2nIDhXurfvz8TJ0585PbkzJaZkpK91i8gwIe2bVs/9bBGQ8KX7HZAzgQyaoilyrOOGlqpoqJS\nJrl16xZLlix5auczGEPVqlVTdIiioqKU0LWuXbuyfft2bt26RWRkJK1bty60/IN1F0R+ekqQreVk\n2M6ZDtzIyIiMjAxEBHt7e+Xc0dHR/Prrr0o5w/E504f/m9Dr9TRu3JihQ4dib29Phw4dSEtL49tv\nv8Xd3R2dToefnx+pqakcPHiQn3/+mfHjx+Ps7MyFCxeUkEDINrScnZ1xdHRk8ODBirFuY2PDtGnT\ncHFxwdHRUQmrDQsLw8PDAxcXF1q0aKHocxWE5KN/9cknn+R73+RnCBowfObwaJ97zvJGRkbKtuEe\nM2BmZoZIBpD59zu3ycy8ibW1dZ6yj0pcXBzz5s1j9+7dREdH06lTp1y6YTm1yHr16sW2bdvYunUr\nrq6uVKtWrcjnK+88LMV/ccoJPAolpYv4OBRXRlEVlfKIasipqKiUSSZNmsT58+dxdnZmwoQJjB8/\nHgcHBxwdHVm/fv0T19+qVSs2b95MWload+/eZcuWLVStWhUbG5tcGkaGxBJVq1bF1dWVMWPG0Llz\nZzQaDebm5gWWN2BhYYGVlZWypuWHH37Ay8tL2R8UlL2WY//+/VhaWiprYnKS3yDL1taW+Ph4JW14\nRkYGsbGx+V6r4fgH9Ziedc6dO8fo0aM5fvw4lpaWbNy4kV69enHkyBGioqKws7Nj+fLlNG/enK5d\nuzJnzhwiIyOxsbFR6khLS8Pf358NGzYQHR1Neno6S5cuVfbXrFmTiIgIhg0bxpw5c4DsNOj79+8n\nIiKC6dOnM2nSpELbmZ/+lVarZd++fYpn7NatW8D/s3fmcTWm7x//nNM+KhUx9mJIdLZOe0onJOsw\nCQ2GxBCa0XztQ5iGYUbGMpbxG1LIbmxjzKCibO0LKaZ0zIwtE5UWWq7fH815ptOCSJv7/Xr14jzn\nfu7nfpZzzr1c1+dT3qFXdOLDw8PRunVraGtrV6lTcc8dHBxw9OhRFBUVIT8/Hz///DPn2fWqnXct\nLS107NgeKipPoaFhDBWV0/D2nqo0EHwVKopp5ObmQltbGzo6Onjw4IHSJERlNDQ0MHDgQHh7e8PT\n07NWx2zqKO7RyyT+69JO4FVoTIOnurL2YDCaImwgx2AwGiWrVq1Ct27dEBcXB2trayQmJiI5ORln\nzpzB3LlzOWGP10UikWDMmDEQCoUYMmQI51u0Z88ebN++HWKxGGZmZjh+/Di3z5gxY7Bnzx6MHTuW\n2/ai8gp27tyJOXPmQCwWIzExEX5+ftx7mpqaMDc3x4wZM7Bjx45q28rj8SCXyyEQCLhtampqOHTo\nEMaMGYMPPvgAEokEly9f5sorOHXqFDcDL5PJkJKS8s6InRgbG3PXTCqVIjMzE8nJyXB0dIRQKERI\nSAiuX7/+wjrS0tLQtWtXdOvWDUB5aOyFCxe490eOHMnVrxh0PXnyBKNGjYJAIICvr2+NA2wFNflf\nbdu2DSNHjoREIuGeuaVLlyI2NhYikQiLFi1CcHBwtXUqngGJRIJJkybB0tIStra2+PTTT7mwvOp8\n5mpCT08PSUnxsLbuBm1tLRw/fhQ3btx46b4V36vo/yUUCiEWi2Fqaorx48dzocc11Tdu3DioqKhw\nIcbvCoprER4eDpFIBHNzcxw4cACzZ89Wet/V1RXFxcXo3bs3Fi1aBFtb2yp1VGbdunXYtGkTRCIR\n7t27V6t2NbbBU20EZBiMZkVtk+rq+g9M7ITBYFRDZmYmCQQCIiLy9fWlwMBA7r1PPvmETpw40UAt\nqzucnJwoNjb2lcpWvB61YdKkSXTo0KFa7aMQ3WjKVL5ea9asoWXLlpGxsTEnqrFz507y9PQkImVR\njoqvExMTydHRkdt+7tw5cnNzIyJloYmYmBiSyWTcvhs3buTaYWxsTETlYifDhg17W6fcrFmzZg35\n+fk1dDMYlXj48CFFRUUxYREGow7Aa4idsBU5BoPR5KBm4j35slWQypSUlCjlfBUVFSnlci1YsAC9\ne/eGWCzGvHnzuNyv//3vf+jZsydiYmKQmJhYoxWCr68vrKyssGLFCnTt2pWzOcjLy1N63VSo7jl5\n+vQp3n//fRQXFyuFk9UUdmpiYgK5XI6MjAwA5aGxTk5OLzxuTk4OOnToAAAIDAx8gzNoPDSUzHxW\nVhZkMhkCAwPx+eef1+uxX5eysrKGbsIrURf3tC7sGBgMxuvDBnIMBqNRoqOjg7y8PADleT779+9H\nWVkZsrKyEBERwYVCNmVCQ0Nhbm7+yuVv3brF5Xzp6enh8OHD3GAwOzsbR48exfXr15GQkIDFixfD\n1tYWZmYC3Lv3D+7fbwFHx4FcLlhCQgLMzMyU1BmLi4sRFRUFPz8/yGQy/PLLLwDK1QXd3NygoqJS\ntxfgLVOdkIy/vz+srKzg4OAAU1NT7r2xY8fiu+++g1Qqxe3bt5VEZwIDAzFq1CiIRCKoqKhg2rRp\n1davYN68eViwYAGkUmmT6dS/iIby6FIcNz4+FxkZ9/Dbb2fq5bgvY+TIkbC0tIRAIOD82nR0dIaq\nth0AACAASURBVDBnzhxIJBJcuXKlRo/H6sR26poff/yRy6MMCgrC/fv3q5RhvmsMRjOhtkt4df0H\nFlrJYDBqYNy4cSQQCGjevHk0b948MjMzI6FQSAcPHmzoptU7mZmZ1KNHD+716tWr6euvvyZPT086\nfPgwlZSUkFgsJi8vLzpy5Ag9f/6cHj58SCoq6gQE/OuvdJF4PD4XBpWenk5SqZSIysM8L1y4wNV/\n8eJFzkvM1taW8xtjvDrNIeysoTy6GrM3WGW/tn/++Yd4PB4Xwvwij8eKPnqLFy9+6554Tk5OFBMT\no7StMV9bBuNdBiy0ksFgNCd2796NpKQkzJkzB6NGjUJoaCgSExMxatSohm5ag/AieXkVFRVERUVh\n1KhROHnyJFxdXZGZmQk+XxuA0b+lzACo1igRXlHyfeDAgcjMzMSRI0eQlpaGTz/9tNp9cnJyOBXH\n9evXv5UVhsrI5XJObQ8AYmNjOfGHusTY2BjZ2dmvtW9zWfFoKJn5xiRvX5l169ZBLBbDxsaG82tT\nVVXlvBsrejxKJBKsWLECd+/eBVCualsbsZ1XITg4GCKRCBKJBBMnTsTy5csREBCAw4cPIyYmBuPH\nj4e5uTlOnTqFkSNHVri2DwF8hMZ0bRkMRu1gAzkGg9GoaS4d4rqAqsn5UmwrKCjAkydP4OrqirVr\n1yIpKQlGRkYoK8sHoOgsZgIo5cK8KlshVITH42HChAnw8fHBN998g8jIyGrLVfT7W7duHQoKCmp1\nTq8Tenj79m2EhIRwr6VSKdatW1frel5GbXMYFVQ0S87JiUVhYRi8vGbUe35ZXdBQMvONSd6+IjX5\ntWlqaip5stXk8ejp6YnNmzcjKSkJfn5+bzzxkZKSgpUrVyI8PBzx8fFYv349gPJn183NDRYWFggJ\nCUFcXBwGDx6MtLQ06Orq/nttvwfghcZybRkMRu1hAzkGg9FoaU4d4rqgslx8RUPf3NxcDB06FCKR\nCI6Ojvj+++9haGiIJUsWgcdbDj7/PWhoOOKbb1ZixYoVVawQapJ8z87Oxvr166Gjo4OUlBSYmppC\nR0cHenp6+OCDD+Dk5ISMjAx06tQJf/75J2QyGfr16wcA+P3332FnZwcLCwuMGTOGG+QZGxtjwYIF\nsLCwwKFDhyCTybBgwQJYW1ujZ8+enOeeXC6Ho6MjLCwsYGFhwXljLVy4EJGRkTA3N8f69etx/vx5\nDBs2DED5wHLkyJEQiUSws7PjDNqXL18OLy8vyGQyfPDBB9i4cSN3ntXlPAGvL6rTmFeTaktDycw3\nNnl7BTX5tVV8Vl7k8ViT2M7rEhoaCnd3d84kXU9Pr0qZim2bMGECTp8+jY0b14DHOw0dncWN5tq+\nLZpDniqDUSO1jcWs6z+wHDkGg1EDUVFR1LKl+b95HOV/uroSioqKauimNSleJ1dLW1ubvvnmGxoy\nZAgJBALS0dEhHx8fWrJkCenp6dGdO3eosLCQzM3NqWvXrkREZGxszOUAPXr0iBwdHamgoICIynP6\n/P39iahctv+7776j9evXk6mpKbVt25Z8fX2pX79+1K1bNxIIBDR16lRKSEigZ8+eERHRrVu3yMLC\ngojKZfwtLS1p9erV3GuFrL+Pjw999dVXREQ0f/58at26NRERLVu2jOzt7am4uJgePXpErVq14mwW\nKuc8Kc6hor1Aba93c8tBaqh8v/o8bmZmJpmZmb2wzM2bN0kkElGvXr1o5MiRZGFhQW5ubqSjo6NU\nTmFbIRKJyMzMjH766SciItqyZQsZGxuTtbU1ffbZZ5z9xeuyceNGWrx4sdK2ZcuWUUBAABFVtTi5\ne/cuSaVS2rJlC/n4+DT6HM7vvvuOs/KYPXs2OTs7ExFRaGgojRs3jry9vcnCwoLMzMxo2bJl3H5G\nRkY0f/58kkqltH///gZpO4NRW/AaOXKqDTyOZDAYjBpRDq8SgoUAvR6Ghoa1mm3fu3c/nj59ii+/\nXAZVVXW0bVs+229ra4tFixbB0NAQz549g6amJnr16oXz588DQMUJOly5cgUpKSmwt7cHEaG4uBh2\ndnbcMcaMGQMXFxecO3cO48aNg6mpKa5du4ZLly6hT58+2LZtG3JzczFlyhQkJCRARUUFt27d4vZ/\n//33MW/evCptj4yM5OwYTE1NUVRUhKdPnwIAhgwZAlVVVbRq1Qpt27bFgwcP0L59e6xbtw5Hjx4F\nAC7n6U1UURWrSV5eMqipdUFxsbzJr3jU9hlqqsd9WTjt33//jU6dOuHEiRMvLCcUCrnPRUWmT5+O\n6dOnv1EbK+Ls7IyPPvoIvr6+MDAwwOPHj5Xer2yr0a5dO7Rv3x4rVqzA2bNnYWJiUmdteRs4ODhg\n7dq1mDVrFmJjY/H8+XOUlpYiIiICffv2hbu7O/T09FBWVoZ+/frBzc0NZmZmAIDWrVsjJiamgc+A\nwXi7sNBKBoPRaGms4VXNGUU4K9ACZWVFeP78AP76628QETw8PPDNN99AQ0MDgwcPRnh4OFRUVEBE\nyMnJ4ewigPJBnYuLC5cnNHnyZFy+fBlCoRC5ubnw8/NDRkYGBg0ahDt37sDf3x9RUVHo378/CgsL\nIZPJMHfuXLz//vv49ttvwefzkZ+fjwEDBuD06dO4fPkyfHx8AACXLl1CREQEpFIp/vjjD/zzzz/V\nnltFsRg+n4+SkpIac57eFA+PMZDLU3H27I+Qy1Ph4THmjet815HL5TA1NcX48ePRq1cvjB49GkVF\nRTh37hzMzc0hEokwZcoUFBcXAygP4Z0/fz6EQiFsbGw4L8CK3otA+WCnumO9jbBeY2NjzJkzp87C\nw3v16oUvv/wSffv2hUQiwf/+9z+lweikSZMwffp0mJub49mzZwDKQ6Y7derU6AdxQHn+a2xsLPLy\n8qChoQFbW1tER0cjIiICDg4O2LdvH6RSKSQSCVJSUrgQVqB8sojBaO6wgRyDwah3hg4dys0SKzpR\ncrkcAoGgSlnWIa5f/svvUnQGTcHjqaGsrAy3b99Gu3btYGxsjA8//BBJSUlQU1NDUVERHj9+jPz8\nfO6+2tjY4OLFi0hPT0dcXBwCAwOxd+9eXL58GXl5efD09ESHDh0QFhaGzp07Y8mSJXB0dERYWBjU\n1NQAlOcT6ejo4NNPP4WHhwf4fD4OHjwITU1NlJSUcB1WoVAIBwcHxMbGwsrKijOOvnHjBrS0tKCt\nrV3j+daU81QXMLPkuictLQ2zZs1CSkoKdHV1ERAQAE9PTxw8eBCJiYkoLi7mVFQBQF9fH0lJSZg5\nc2aNhuLVrcK1adMGZ8+eRUxMDPbt28dNGqxatQoODg6Ii4vj6lPsv3TpUpibmyMxMRErVqzAhAkT\nlNrt5TUV9+8/wdq169C5s0mdCTdNmDABycnJiI+Px44dO+Dn54cvvvgCAPDRRx8hNTUVcXFx3ERG\nZGQkpk6dWifHftuoqqrCyMgIO3fuhL29PRwcHBAWFob09HRoamoiICAAYWFhSExMxODBg5UmYSqq\n8DIYzRUWWslgMOqU9evXY9q0adDU1KyxzMmTJ7n/VxbwqI6GCut6F/kvnFUhkLAJZWVFKCriw8PD\nA3///Tfu3r2LwsJCLFq0CGZmZmjTpg3EYjGeP3+Onj17om3btpDL5XB1dYVIJMKzZ8+goaGBzMxM\n6OjooLS0FLNnz8Zff/2Fv/76CxEREdDS0sLFixfh6uqK0tJSAOUrKitWrEBJSQk2bdqE9957D3p6\neujUqRN4PB727t2Lbt26QVdXF1euXIFQKERRURFyc3MhEom4FbzqUDxrrq6u2Lp1K3r37g0TExPY\n2tpWKcNoPHTu3Bk2NjYAyleW/P390bVrV3Tr1g0AMHHiRGzevBmfffYZgHKjdwDw8PDgBjevQnFx\nMaZNm1ZtWG9NVAzrlclkyM7O5sJ6nZyc8OmnPigqOg/AA0VF6+HlNQb9+zvX23dbVlYWHB0dYWBg\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P22fOnDmQSCRYuXIlRo4cydV39uxZLm+5LlFVVYWfnx8sLS0xcOBAmJqa1vkx3lV0dHSQ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\nT2wx5roAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcfc8a45310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(embeddings, labels):\n",
    "  assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'\n",
    "  pylab.figure(figsize=(15,15))  # in inches\n",
    "  for i, label in enumerate(labels):\n",
    "    x, y = embeddings[i,:]\n",
    "    pylab.scatter(x, y)\n",
    "    pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',\n",
    "                   ha='right', va='bottom')\n",
    "  pylab.show()\n",
    "\n",
    "words = [reverse_dictionary[i] for i in range(1, num_points+1)]\n",
    "plot(two_d_embeddings, words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QB5EFrBnpNnc"
   },
   "source": [
    "---\n",
    "\n",
    "Problem\n",
    "-------\n",
    "\n",
    "An alternative to skip-gram is another Word2Vec model called [CBOW](http://arxiv.org/abs/1301.3781) (Continuous Bag of Words). In the CBOW model, instead of predicting a context word from a word vector, you predict a word from the sum of all the word vectors in its context. Implement and evaluate a CBOW model trained on the text8 dataset.\n",
    "\n",
    "---"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "default_view": {},
   "name": "5_word2vec.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
